Artykuły w czasopismach na temat „Electric power consumption – united states – forecasting”

Energy forecasting based on univariate time series has long been a challenge in energy engineering and has become one of the most popular tasks in data analytics. In order to take advantage of the characteristics of observed data, a partially linear model is proposed based on principal component analysis and support vector machine methods. The principal linear components of the input with lower dimensions are used as the linear part, while the nonlinear part is expressed by the kernel function. The primal-dual method is used to construct the convex optimization problem for the proposed model, and the sequential minimization optimization algorithm is used to train the model with global convergence. The univariate forecasting scheme is designed to forecast the primary energy consumption of the electric power sector of the United States using real-world data sets ranging from January 1973 to January 2020, and the model is compared with eight commonly used machine learning models as well as the linear auto-regressive model. Comprehensive comparisons with multiple evaluation criteria (including 19 metrics) show that the proposed model outperforms all other models in all scenarios of mid-/long-term forecasting, indicating its high potential in primary energy consumption forecasting.

As an essential measure to mitigate the CO2 emissions, China is constructing a nationwide carbon emission trading (CET) market. The electric power industry is the first sector that will be introduced into this market, but the quota allocation scheme, as the key foundation of market transactions, is still undetermined. This research employed the gross domestic product (GDP), energy consumption, and electric generation data of 30 provinces from 2001 to 2015, a hybrid trend forecasting model, and a three-indicator allocation model to measure the provincial quota allocation for carbon emissions in China′s electric power sector. The conclusions drawn from the empirical analysis can be summarized as follows: (1) The carbon emission peak in China′s electric power sector will appear in 2027, and peak emissions will be 3.63 billion tons, which will surpass the total carbon emissions of the European Union (EU) and approximately equal to 2/3 of the United States of America (USA). (2) The developed provinces that are supported by traditional industries should take more responsibility for carbon mitigation. (3) Nine provinces are expected to be the buyers in the CET market. These provinces are mostly located in eastern China, and account for approximately 63.65% of China′s carbon emissions generated by the electric power sector. (4) The long-distance electric power transmission shifts the carbon emissions and then has an impact on the quotas allocation for carbon emissions. (5) The development and effective utilization of clean power generation will play a positive role for carbon mitigation in China′s electric sector.

Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

The editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (China, Netherlands, Spain, Tunisia, United States of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and Czech Republic), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust μ-synthesis for microgrid, and neuro-fuzzy systems in energy storage.

This study contributes to the body of literature on modeling and predicting gasoline demand by using nonlinear econometric techniques. For this purpose, dynamic model averaging (DMA) and Bayesian model averaging (BMA) combined with Artificial Bee Colony (ABC) are used to forecast gasoline consumption in the United States. The article’s independent variables include demographic characteristics, economic activity, income, driving expenditures, automobile price, and road availability for annual data from 1960 to 2020. In the proposed model, not only may the coefficients and elasticity of a predictor of gasoline demand change over time, but other sets of predictors can also emerge at different periods. Moreover, this study aims to automate the process of picking two forgotten variables of the DMA model using the ABC model. Our findings indicate that dynamic model averaging significantly improves forecasting performance when compared to basic benchmark techniques and advanced approaches. Additionally, integrating it with an Artificial Bee Colony (ABC) may result in improved outcomes when time-varying forgetting variables are present. The findings of this research provide policymakers in the fields of energy economics and the environment with helpful tools and information.

Natural gas is one of the main energy resources for electricity generation. Reliable forecasting is vital to make sensible policies. A randomly optimized fractional grey system model is developed in this work to forecast the natural gas consumption in the power sector of the United States. The nonhomogeneous grey model with fractional-order accumulation is introduced along with discussions between other existing grey models. A random search optimization scheme is then introduced to optimize the nonlinear parameter of the grey model. And the complete forecasting scheme is built based on the rolling mechanism. The case study is executed based on the updated data set of natural gas consumption of the power sector in the United States. The comparison of results is analyzed from different step sizes, different grey system models, and benchmark models. They all show that the proposed method has significant advantages over the other existing methods, which indicates the proposed method has high potential in short-term forecasting for natural gas consumption of the power sector in United States.

This Article explores efforts to address challenges involving wind power intermittency in two United States power regions: the South- west Power Pool (“SPP”) and the Electric Reliability Council of Texas (“ERCOT”). SPP and ERCOT are good case studies regarding these issues because each has among the strongest wind resources in the country, most of which are in isolated, sparsely populated areas and need long transmission lines to reach major load (electricity consumption) centers. Those circumstances increase the challenge of integrating intermittent wind generation into the electric system (grid).

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

This paper was designed to examine the predictive power of grafted polynomial functional form in forecasting rice consumption in Nigeria with a linear model used as bench mark. Data on rice consumption trend in Nigeria from 1961 to 2011 elicited from the United States department of Agriculture foreign Agricultural services were utilized in this study. It was observed that rice consumption did not correlate linearly with trend over the entire sample period and this necessitatedthe grafting of a Linear – Quadratic – Linear model. The ex-post forecasts of the grafted model had lesser forecast error and were closer to the observed values than that of the linear model which had greater forecast error. Therefore, the predictive performance of the grafted model is more reliable in forecasting future trend of rice consumption in Nigeria for planning purposes.

The plug-in hybrid electric vehicle not only has the advantages of low emissions from electric vehicles, but also takes advantage of the high specific energy and high specific power of petroleum fuels, which can significantly improve the emissions and fuel economy of traditional vehicles. Studying its comprehensive energy consumption evaluation method is an important part of analyzing the economics of plug-in hybrid electric vehicles. This paper first puts forward the concept of statistical energy consumption and then proposes an innovative calculation method of plug-in hybrid electric vehicle energy consumption based on statistical energy consumption by referring to and analyzing the energy consumption test regulations of the United States, the European Union, and China. Given the two use case assumptions of charge depleting mode priority and charge sustaining mode only, considering the fuel consumption and the energy consumption that converts electrical energy consumption to fuel consumption, the probability density function of travel mileage distribution and energy consumption is derived. Finally, the interpretation and analysis of statistical energy consumption evaluation results are carried out.

ABSTRACTWind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.

Introduction. Reducing the energy intensity of production is one of the directions of development of enterprises, regions, states, as it affects the level of economic development and environmental well-being. One of the methods that contribute to the rationalization of the use of electric energy is forecasting. The purpose of the study is to develop a model for forecasting electricity consumption, which allows obtaining a reliable forecast of electricity consumption. To achieve this goal, an analysis of literary sources reflecting the current trends of predictive analytics in solving the problem of forecasting electricity consumption was carried out. Materials and methods of research. Singular spectral analysis and deep learning algorithms (multilayer perceptron) were used in the work. At the initial stage of the study, a model of a fully connected neural network (multilayer perceptron) was built, the prediction accuracy of which was more than 94%. Then the algorithm of singular spectral analysis was implemented, and the initial time series of power consumption was decomposed into additive components. The resulting decompositions were used to create a hybrid forecasting model. Research results. A number of experimental studies have been conducted that meet the goal of developing models that most accurately approximate experimental data. The result of the study are two forecasting models: an artificial neural network model and a hybrid model that includes a singular spectral analysis to decompose a number of power consumption into trend and harmonic components and an artificial neural network to predict them. Discussion. With the help of the hybrid model, an accurate result was obtained – the average absolute percentage error for the year is 4.04%. Forecasting was carried out for a year (by months) using data from the previous two years as a training subset. The neural network model provides less accuracy in predicting power consumption over 12 months compared to the hybrid model. Conclusion. Thus, the use of singular spectral analysis made it possible to reduce the magnitude of the forecast error by almost 1%, which confirms the effectiveness of the method and the relevance of the development of hybrid forecasting models. Resume. As a result of the conducted research, it was revealed that one of the most popular and effective forecasting methods is singular spectral analysis and the use of various deep learning algorithms. The developed hybrid model of electric energy consumption for a mining and metallurgical enterprise allows you to build a forecast of electricity consumption, the accuracy of which is more than 95%. Suggestions for practical application and directions for future research. The research results can be useful in the process of production management, since an accurate forecast of electricity consumption is important when planning production, scheduling equipment repairs and maintenance of the electrical network. The prospect of further research is to increase the forecasting horizon, the volume of the initial sample and taking into account additional factors characterizing the technological process.

In this paper, various modern power engines developed by the American, Japanese, and European automobile industries will be compared. Specific data, including the efficiency, emission rate of nitrogen oxides (NOx), fuel consumption, and electronic vehicle technology, will be developed. Since the first invention of the automobile engine in the late 19th century, companies came up with unique innovations, including its structure, control systems, and additional mechanical installations to improve efficiency and reduce emissions. Numerous companies, including Ford, Toyota, and Mercedes-Benz, compete in the automobile industry to improve their engine’s efficiency and emission rates to create a clean environment. In addition, each country has its regulations on emission rates and automobile structure. Therefore, to meet these regulations, the structure and the system of the engines vary between companies in different countries. A variety of variable valve timing (VVT) systems, which is a mechanical part installed in the engine, are being developed by several companies. The VVT controls the opening and closing of the air inlet valve and the exhaust valve, which improves the reduction of fuel consumption and thermal efficiency. Furthermore, changing the engine structure is also another method that automobile companies are developing. Changing the engine’s shape can improve the vehicle’s performance (e.g., the engine vibration while running, the power output, and the smoothness of driving). Due to the emissions caused by petrol and diesel engines, the electrified vehicles have been developing to achieve a cleaner environment. This includes battery electric vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, and fuel cell electric vehicles. By comparing these features in the engine, it is possible to understand what the companies in the US, Japan, and the European countries are working on to improve their engines and provide a clean environment.

Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).

The creep trend method is used for the analysis of the development of electric car production in three regions: The United States, the European Union and Japan. Based on vehicle registration and population growth data for each year the creep trend method using historical data for the years 2007–2017 is applied for forecasting development up to 2030. Moreover, the original method for calculating the primary energy factor (PEF) was applied to the analysis of power engineering systems in the regions investigated. The assessment of the effects of electromobility development on air quality has been performed, reduction values for pollutant and greenhouse gas emissions have been determined, which was the main objective of this manuscript. Mitigation of air pollutant emissions, i.e., carbon dioxide (CO2), carbon monoxide (CO) and nitrogen oxides (NOx) was estimated and compared to the eventual expected increase of emissions from power plants due to an increase of the demand for electricity. It can be concluded that electricity powered cars along with appropriate choices of energetic resources as well as electricity distribution management will play the important role to achieve the sustainable energy economy. Based on the emission reduction projections resulting from the projected increase in the number of electric cars, (corrected) emissions will be avoided in 2030 in the amount of over 14,908,000 thousand tonnes CO2 in European Union, 3,786,000 thousand tonnes CO2 in United States and 111,683 thousand tonnes CO2 in Japan.

California’s significant role as the second-largest consumer of energy in the United States underscores the importance of accurate energy consumption predictions. With a thriving industrial sector, a burgeoning population, and ambitious environmental goals, the state’s energy landscape is dynamic and complex. This paper presents a comprehensive analysis of California’s energy consumption trends and provides detailed forecasting models for different energy sources and sectors. The study leverages ARIMA and ARIMAX models, considering both historical consumption data and exogenous variables. We address the unique challenges posed by the COVID-19 pandemic and the limited data for 2022, highlighting the resilience of these models in the face of uncertainty. Our analysis reveals that while fossil fuels continue to dominate California’s energy landscape, renewable energy sources, particularly solar and biomass, are experiencing substantial growth. Hydroelectric power, while sensitive to precipitation, remains a significant contributor to renewable energy consumption. Furthermore, we anticipate ongoing efforts to reduce fossil fuel consumption. The forecasts for energy consumption by sector suggest some decline in the commercial and residential sectors, reflecting California’s recently declining population and the shift away from brick-and-mortar shops and offices to online websites and remote work. In contrast, the industrial and transportation sectors are expected to experience some growth until they return to more constant pre-COVID levels.

The purpose of research was to analyze ecological development in EU countries during 2000-2012. Six primary variables have been applied, namely: the share of forest area in total land area, emissions of sulphur oxides (SOx) in agriculture, emissions of nitrogen oxides (NOx) in agriculture, electric power consumption per capita, the share of alternative and nuclear energy in total energy use and the share of area under organic farming in total agricultural land area. The authors have developed a synthetic index, which enabled the statement that Latvia, Finland, Austria, Sweden and Estonia are leading EU countries in terms of ecological development; the worst situation is in France, Poland, Malta, Spain and in the United Kingdom.

The techno-economic analysis/assessment (TEA) tool H2A-Lite (Hydrogen Analysis Lite Production) of the United States National Renewable Energy Laboratory (NREL) is applied for computing the levelized cost of hydrogen (LCOH) in the Sultanate of Oman, in the case of utilizing polymer electrolyte membranes (or proton exchange membranes, PEM) in combination with photovoltaic (PV) solar systems. Fourteen parameters (assumptions) were adopted, which include: purchased photovoltaic (PV) green electricity at a fixed rate (tariff) of 0.025 OMR/kWh (0.065 US$/kWh; 1 OMR ≈ 2.6 US$), 64 kWh/kgH2 (64 kWe/(kgH2/h)) specific electricity consumption by electrolyzers, OMR 384.6 (US$ 1,000) capital cost per kWe (kilowatt electric) of PEM electrolyzer input-electric capacity, 1 tonne (metric ton; 1,000 kg) of green hydrogen per day (nameplate production capacity), 90% utilization factor, 5 employees with equal individual annual salaries of OMR 26,923 (US$ 70,000), 20 years project lifetime, and straight-line depreciation. The results show that the LCOH is approximately 2.17 OMR/kgH2 (5.63 US$/kgH2). The corresponding electrolyzer nameplate electric-input capacity is 2.667 MWe (megawatt electric), with actual (not nameplate value) electrolyzer input electric power of 2.400 MWe, and actual (not nameplate value) annual electricity consumption of 21.024 GWh (gigawatt-hours). A sensitivity analysis, with 10% uncertainty, is reported for seven modeling parameters.

With the increasing proportion of electricity in global end-energy consumption, it has become a global consensus that there is a need to develop more environmentally efficient renewable energy generation methods to gradually replace traditional high-pollution fossil energy power generation. Renewable energy generation has become an important method of supplying power across the world. Therefore, the accurate prediction of renewable energy generation plays a vital role in maintaining the security of electricity supply in all countries. Based on this, in our study, a novel dynamic accumulation grey seasonal model is constructed, abbreviated to DPDGSTM(1,1), which is suitable for forecasting mid- to long-term renewable energy generation. Specifically, to overcome the over-accumulation and old information disturbance caused by traditional global accumulation, a dynamic accumulation generation operator is introduced based on a data-driven model, which can adaptively select the optimal partial accumulation number according to the intrinsic characteristics of a sequence. Subsequently, dummy variables and a time trend item are integrated into the model structure, significantly enhancing the adaptability of the new model to the sample sequence with different fluctuation trends. Finally, a series of benchmark models are used to predict renewable energy generation in China, wind power generation in the United States, and hydropower generation in India. The empirical results show that the new model performs better than other benchmark models and is an effective tool for the mid- to long-term prediction of renewable energy generation.

Abstract. We could get many helpful information and results from satellite remote sensing images and aerial images, including disaster monitoring, grid hidden danger identification, and electricity consumption management. In the recent years, novel computer vision and deep neural network have got a lot of attention in many fields because of mimicking mammalian cognitive mechanism as much as possible. With the in-depth of mammalian cognitive and motor mechanisms research, people trend to adopt these reliable and efficient methods for power grid management and maintenance.For utilizing computing resources and improving analysing efficiency flexibly, we propose an assessing and verification framework based on bio-inspired perception and understanding, which summarizes the most appropriate image scale in the electric facilities place recognition. The proposed framework consists of different scenes aerial images datasets, several electric facilities place recognition methods, and credible evaluating methods mimicking mammals. Firstly, we gather satellite remote images and aerial images of sufficient electric power facilities in the United States via Google Earth and other public datasets. Then, several typical place recognition methods are adopted to testing recognition ability of multi-scale perception results, like SAD, NetVLAD, and GIST descriptor. To get more reliable result, multi-units and multi-scenes experiments are implemented roundly. After all experiments and evaluations, we could get that the most appropriate image scale is 1000 m size and the highest recognition accuracy of electric power facilities location is 500 m. Conclusion in our article shows the recommended perception form and scale closest to human cognition in the power grid management and maintenance.

On the way to EU membership, Ukraine joined the "European Green Deal", which aims to achieve zero greenhouse gas emissions and zero environmental pollution by transitioning from the use of fossil fuels to renewable sources of energy and raw materials in the member states of the European Union by 2050. The concept of the "green" energy transition of Ukraine by 2050 was formulated, which involves a change in approaches to the development of energy with an emphasis on the problem of combating climate change and sustainable economic development. When achieving the goals of the "green" transition, it is important to understand which factors have the greatest impact on electricity consumption. It is promising to include such factors in electricity demand forecasting models. These models will become increasingly important to ensure network reliability and efficiency. Identifying the factors that affect energy consumption can drive innovation in energy-efficient technologies and practices. The article concludes that electricity consumption in Ukraine is affected by the same main factors as in other countries of the world. The desired reduction in electricity consumption can be achieved both by purely economic measures (increasing tariffs) and by more acceptable, technological measures. However, in the conditions of war, there are additional difficulties, such as the difficulty of forecasting the structure, state, and development of the energy system on the energy market. A significant part of the population now has more urgent problems than energy conservation, so the role of the state in this issue is increasing. Ukraine's energy system has already suffered significant destruction, and this process is ongoing and may continue for an indefinite period of time. Despite the difficulties, the lost power system equipment is gradually being replaced with new, more technologically advanced equipment. Some of the enterprises that had significant consumption were either lost or failed. The most adapted to new problems turned out to be the trade sector and the service sector and part of the population. Here, there is a rapid transition to the use of own generators, solar panels and electric transport, which reduces the consumption of electricity from the state power system. Industry, taking into account the funds required for this, will replace technologies with more energy-efficient ones after the end of the active phase of the war. Keywords: "green" transition, energy efficiency, electricity consumption, electricity demand forecasting, energy system.

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set.

Power Quality (PQ) has been an important topic since the creation of distribution systems. The deployment of the Advanced Metering Infrastructure (AMI) provided an important tool to measure the PQ of the electric power in the consumption points. One of the smallest secondary distribution systems in terms of power consumption is the single-phase split-phase system (120 V/240 V) that countries such as the United States, Canada, and some countries of central and south America have. Due to its size, this secondary distribution system is more prone to PQ issues. To that end, an experimental set-up was built by the authors so the distribution system from the Low Voltage (LV) transformer to the final appliances of the different houses was emulated. The aim is to capture the currents and voltages observed by the smart meter located at the entrance of the house and look for the different responses. A combination of real and dummy loads was installed in the set-up, so real noise could also be simulated. The set-up was totally automated by an industrial controller and relays, and it produced a very detailed dataset that could be used for multiple purposes.

The Nigerian power problem resulted to incessant and erratic supply of electricity and this has destroyed many industrial processes in the country. It has reduced productivity and has increased unemployment rate in the country to over 50million (this figure is over 70% of Nigerian youths). This has led many of the youths in the country to crime. It has led to the deaths of many innocent people in the country. As of 2016, the electricity energy consumption in the world from the world fact book revealed that the average power per capita (watts per person) in the United States is 1,377 Watts. In Canada, it is 1704 Watts per person and in South Africa; it is 445 Watts per person and in Australia, average power per capita (watts per person) is as high as 1,112 Watts. Whereas, the average electricity consumed in watts per person in Nigeria is just 14 Watts. Unfortunately, this has put the country in a rank of 189 out of 219 countries estimated. In this research work, a Hybrid Electric Power System (HEPS) which comprises Hydro Electric Power Plant (HEPP) and Diesel Generator (DG) was modelled and a control algorithm was established to improve the performance of the system. Hybrid power system mathematical and Simulink models were developed. The output power of the developed Simulink model was be optimized using optimum power point optimization techniques and control algorithms. Simulink models of the two components of the Hybrid Electric Power System were produced using MATLAB/Simulink software. The develop Simulink models was interconnected and final model was developed. The results obtained revealed that the problems associated with conventional methods of power generation was overcomed by the development of this renewable and non-renewable energy resources Hybrid Electric Power System (HEPS) models.

Federal and state governments in the United States (US) are promoting the transition from traditional Diesel School Buses to Electric School Buses (ESBs). This would prevent the emission of deleterious air pollutants that affect students and communities while simultaneously contributing to a reduction in greenhouse gases, aiding in the fight against climate change. However, due to their significant size and long routes, ESBs require large batteries with significant electricity demand. If this additional electricity demand is supplied to hundreds of thousands of EBSs at peak consumption times, the strain on the grid may be detrimental, while transportation costs for schools could dramatically increase. Furthermore, if EBSs are charged using traditional hydrocarbon generation, the environmental benefits of these projects may be significantly reduced. Therefore, applying renewable energy presents a host of synergistic opportunities to reduce emissions while providing inexpensive electricity to schools. Solar energy is abundant in large portions of the US, potentially providing many schools with ample inexpensive and sustainable electricity to power their transportation equipment and meet other requirements at their facilities. This research developed a novel framework to integrate publicly available big data provided by federal and state agencies in the US, as well as National Laboratories, to provide stakeholders with actionable information to develop EBS grid-to-vehicle (V2G) systems across the US. Geographic Information Systems, data analytics and Business Intelligence were applied to assess and characterize solar energy generation and consumption patterns. The novel integration of the systems in the proposed framework provided encouraging results that have practical implications for stakeholders to develop successful and sustainable ESB V2G facilities. These results identified many schools across the US that would significantly benefit from the use of solar energy and be able to supply their local communities during idle times with renewable energy through V2G. The renewable energy resource would be capable of charging ESBs at a low cost for operational availability as required. The results indicate that the proposed ESB V2G system will provide significant benefits to both schools and their local communities. The feasibility of the proposed endeavor was validated by the results of the study, providing both school and solar energy stakeholders with insights into how to better manage such a complex system.

The U.S. Army Corps of Engineers has pursued continual refinement in the waterway traffic forecasts used in project economic analyses. The multiscenario forecasting of utility steam coal described in this paper, based on the work of Hill & Associates, Inc. (H&A), is a substantial refinement of previous forecasting efforts. H&A prepares 20-year steam coal forecasts on the basis of the interaction of two major linear programming models: the National Power Model (NPM) and the Utility Fuel Economics Model (UFEM). The NPM is a utility industry model that dispatches all electric generating plants in the United States (coal and noncoal), producing forecasts of generation by fuel type within a context of transmission and environmental constraints. The UFEM makes use of highly detailed coal supply data (for approximately 98 coal types) to allocate fuels among plants and units. These two models develop forecasts of coal demand for electricity generation, by type of coal, which were then “mapped” to the waterway. H&A prepared separate forecast scenarios based on three alternative environmental regulatory futures. The first is a continuation of existing law (in this case, the national ambient air quality standards). The second H&A scenario reflects implementation of the administration's Clear Skies Initiative. The final H&A scenario is an implementation of the Clear Skies Initiative without the proposal's severe mercury restrictions.

Electricity has a high impact in the activities and sectors of any economy. A considerable amount of studies relates the importance of electricity to economic growth of nations. Dominican Republic, though, has been in a process of energy reform, has not yet developed a stable electric delivering. This study is firstly conducted to ensure an overview of the electricity sector of Dominican Republic, describing the energy mix, national electricity situation, and main concerns on the sector reform that have been in process including other Latin American countries. Secondly, the paper aims to determine the elasticity of demand on energy generation sector through log linear regression method; analyzing the variation of price and quantity of demand interaction. Related researches concerning elasticity of demand in other countries such as Australia, Israel, China and United States, shows close similarity to our result, where elasticity tends to be inelastic (0.57) but not perfectly inelastic to variation on price. While the energy market structures by sectors may be complex, customers’ response to pricing signals can promote efficient investment in the long-run term, help mitigate short-run market power by generators and transmission owners, reduce price spikes, low price volatility, and consequently support price mechanism.

Tropical storm Isaias (2020) moved quickly northeast after its landfall in North Carolina and caused extensive damage to the east coast of the United States, with electric power distribution disruptions, infrastructure losses and significant economic and societal impacts. Improving the real-time prediction of tropical storms like Isaias can enable accurate disaster preparedness and strategy. We have explored the configuration, initialization and physics options of the Weather Research and Forecasting (WRF) model to improve the deterministic forecast for Isaias. The model performance has been evaluated based on the forecast of the storm track, intensity, wind and precipitation, with the support from in situ measurements and stage IV remote sensing products. Our results indicate that the Global Forecasting System (GFS) provides overall better initial and boundary conditions compared to the North American Model (NAM) for wind, mean sea level pressure and precipitation. The combination of tropical suite physics options and GFS initialization provided the best forecast improvement, with error reduction of 36% and an increase of the correlation by 11%. The choices for model spin-up time and forecast cycle did not affect the forecast of the storm significantly. In order to check the consistency of the result found from the investigation related to TS Isaias, Irene (2011), Henri (2021) and Elsa (2021), three other tropical storms, were also investigated. Similar to Isaias, these storms are simulated with NAM and GFS initialization and different physics options. The overall results for Henri and Elsa indicate that the models with GFS initialization and tropical suite physics reduced error by 44% and 57%, respectively, which resonates with the findings from the TS Isaias investigation. For Irene, the initialization used an older GFS version and showed increases in error, but applying the tropical physics option decreased the error by 20%. Our recommendation is to consider GFS for the initialization of the WRF model and the tropical physics suite in a future tropical storm forecast for the NE US.

The analysis and process of not only the current states of information objects, but also the prediction of future states with a certain time interval presents a major significance for adaptive information systems. This allows improving the quality and reliability of these systems functioning, reducing the delay in response to external influences, preparing for operations, and increasing the responsiveness and speed of the system. In order to solve this problem, the article proposes a neural network method for forecasting the state of information objects based on the application of machine learning technologies. A formalized algorithm for its implementation in the notation of set theory is presented. A distinctive characteristic of the designed method is the automatic determination of the optimal structure of the neural network, depending on the type of information object. The method also covers the possibility of using the complex of the previous states of the object to improve the forecast accuracy. Practical researches on approbation of the neural network method showed its efficiency and high accuracy. The following popular datasets were used as input data: Individual household electric power consumption, HAR (Human Activity Recognition) accelerometer, as well as gathered data on human relocation. LSTM (Long Short-Term Memory) neural network was applied to conduct the forecasts. The comparison of the developed method with a similar software solution DEvol (DeepEvolution) showed comparable or better indicators in terms of accuracy and time for the problem solution (1.7 times faster on average).

In the late 19th century, the three-phase induction motor was the central element of productivity increase in the second industrial revolution in Europe and the United States. Currently, it is the main load on electrical systems in global terms, reaching approximately 70% of electrical energy consumption in the industrial sector worldwide. During the 20th century, electric motors underwent intense technological innovations that enabled significant performance gains. Thus, this work analyses the performance changes in squirrel-cage rotor three-phase induction electric motors (SCIMs) with mechanical powers of 3.7 kW, 37 kW, and 150 kW and speed ranges corresponding to two poles and eight poles, connected to a low voltage at a frequency of 60 Hz and tested between 1945 and 2020. The study confirms accumulated performance gains of above 10% in some cases. Insulating materials for electrical conductors have gone through several generations (cotton, silk, and currently, varnish). Improvements to the housing for cooling, the bearings, the quality of active materials, and the design were the elements that enabled the high gains in performance. The first commercial two-pole SCIM with a shaft power of 4.4 kW was marketed in 1891, with a weight/power ratio of 86 kg/kW, and until the 2000s, this value gradually decreased, eventually reaching 4.8 kg/kW. Between 2000 and 2020, this ratio showed a reversed trend based on improvements in the performance of SCIMs. More active materials were used, causing the weight/power ratio to reach 8.6 kg/kW. The MEPS (minimum energy performance standards) of SCIMs had an essential role in the performance gain over the last three decades. Data collection was via tests at the Electrical Machines Laboratory of the Institute of Energy and Environment of the University of São Paulo. The laboratory has a history of tests on electrical equipment dating from 1911.

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building’s energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions.

The paper presents the results of a study to determine the rational structure of mini-CHP (Cogeneration Heat and Power Plant) using local fuels types (LFT) for operation as part of the United Energy System (UES) of Belarus with a surplus of electricity generating capacity and dominance of imported types of energy resources (natural gas and nuclear fuel) in the fuel balance. When optimizing the operating modes of mini-CHPs using LFT and operating in parallel with the UES, which has a significant surplus of electricity generating capacity, it is necessary to separate options for existing stations and options for newly built ones. In the first case, due to the fact that the power of the equipment is known, it is advisable to consider two extreme options, i.e., the operation of the heating unit according to an electrical or thermal load schedule. In this case, in order to maintain the daily consumption traffic it is necessary to provide for the accumulation of thermal or electrical energy, respectively. In the case of new construction, the optimized parameter is the power of the generating equipment, so it is advisable to give preference to the option with the maximum number of hours of use of the rated power. In order to increase the economic attractiveness of mini-CHP, options for developing the structure of mini-CHP using LFT with the transition to multi-generation technologies and adaptation to the existing operating conditions of the UES of Belarus have been considered. The results of an analysis of commercially available technologies for storing excess electrical energy are presented in accordance with current and projected (until 2030) cost and operational indicators. For adapting mini-CHP to operate in the UES in conditions of a surplus of electrical power capacity, an electrical energy storage system using hydrogen as an intermediate energy carrier is of greatest interest. To utilize the excess electrical energy consumption from a mini-CHP with a heating ORC unit during the daily dips, a structural diagram configuration using an alkaline electrolysis module for hydrogen production is proposed. The efficiency of energy storage and use technology is considered depending on the specific energy intensity for various electrical energy storage technologies. The use of the two most energy-intensive energy storage technologies is proposed: accumulation based on electrochemical batte-ries and the “electricity-hydrogen” type. During the study, an analysis of the functioning of the ORC-installation Turboden 14 CHP ORC-installation operating as part of a mini-CHP using LFT was carried out. It was revealed that today the installation operates in a wide range of load changes (from 17 to 87 % of the rated electrical power), while the generation of electrical energy from thermal consumption varied in the range from 0.20 to 0.026 MW/MW. Due to the fact that the ORC installation under study is a component of the energy source with a high installed peak thermal power, in the current state there is no direct correlation between the outside air temperature and the generation power of the ORC installation. This circumstance indicates the need to continue the study of heat load trends to build functional models for short- and medium-term forecasting of heat load depending on the time of day and average hourly outside air temperature, which was implemented in the second part of the work.

The diffusion of distributed energy resources can overcome some challenges associated with the historical centralized model of electric power distribution. Decentralized generation by residential solar photovoltaic cells creates the potential for peer-to-peer (P2P) electricity trading, where households can act as consumers and prosumers to buy and sell renewable electricity. P2P energy markets are emerging at locations across the globe, and market performance is affected by various social, economic, and environmental factors. This research applies an agent-based modeling (ABM) framework to simulate electricity trades between heterogeneous households in a decentralized market. The P2P system is tested for 15 locations in the United States that vary in climate parameters and local economic factors. The results from these simulations are compared to assess how differences in climate, demand pattern, retail rate, and irradiance affect market performance. Simulations demonstrate that market outcomes rely on the ratio of prosumers to consumers, environmental factors, and geographic conditions. Battery energy storage overcomes limitations associated with faulty forecasting and improves the flexibility of household-generated solar resources to increase the proportion of production that is sold in the P2P market. The application of the agent-based modeling framework demonstrates how P2P markets can be expected to perform for various locations and can be applied to assess alternative locations for market performance.

The paper describes the characteristics of small modular reactors (SMR), their differences from existing nuclear power plants and the features of their use in modern hybrid electric power systems in combination with renewable energy sources and Nuclear Renewable Hybrid Energy System (NRHES) electricity storage, their advantages and problems of use in the future power system of Ukraine. Prospects for cooperation in this area with companies from the United States of America were considered. Small modular SMR reactors are suitable for electrical systems of various capacities, their modular system allows flexibility and speed of construction, reduces capital investment needs and eases financing requirements. The smaller size and variety of reactors can also mean that they can be built in locations not traditionally suitable for large nuclear power plants and, importantly, near energy-intensive industries or remote communities, i.e. as elements of distributed generation. They can ensure a reliable supply of not only electricity, but also heat. SMRs can also be deployed at decommissioned coal-fired power plants. Taking advantage of existing infrastructure, including switchgear and coal-fired turbines, could reduce SMR construction costs and avoid the need to add new transmission lines from these facilities. Problems with the implementation of such networks are revealed. Attention was drawn to the increased complexity of the system in connection with the use of various sources of generation and processes of distribution and consumption of electricity. The main technical characteristics, features of the reactor design, the station building, safety systems and control systems are given. The study concludes that SMRs have significant advantages for use in modern networks, due to their modular design and modular deployment, to meet a variety of power switching and scaling requirements.

Renewable energies have an essential role in reducing various forms of pollution. The policymakers within the European Union place more and more emphasis on the replacement of internal combustion engine vehicles with electric vehicles in order to reduce emissions. The aim of this research is to analyze the current trends in producing and using renewable energy until 2028 and to estimate the impact of replacing the current internal combustion engine cars with electric cars. The significance of this study emerges from the estimation of the amount of electricity needed to replace current cars with electric cars and if it can be covered from green sources, based on the forecast of green energy until the year 2028. In addition, we also calculate in this study the impact on the public budgets of the European Union member states, as a result of the reduction of excise duties for fuels, following the reduction of their consumption. The research was carried out based on the extensive literature on the subject and data from Eurostat. The data used in this study are from 1990 to 2021. In this research we have used the IBM SPSS application with two of the most used forecasting algorithms: exponential smoothing model and autoregressive integrated moving average (ARIMA), based on the statistical analysis of the historical data. The estimated results showed that the replacement of traditional fuels will lead to an increase of 12.18% for electrical energy, and it could be covered 100% from green sources, if needed, even before 2028. There are many implications of this study for policymakers and the population. The results show that we still need policies to stimulate electricity production from renewable sources. There is a challenge regarding reducing government revenue due to fuel excises, which can be compensated by updating tax policies, with an impact on population and living standards. Furthermore, maintaining and adapting support schemes for electric cars, as well as expanding electric car infrastructure and smart grids are also challenges that need to be addressed by the policymakers and the industry.

Climate change is one of the most preeminent challenges facing the world. Continued fossil fuel energy consumption at the current trajectory will result in a temperature rise of more than 3.5°C by the year 2050. Melting glaciers, rising sea levels, wildfires, floods, and extreme weather events such as heat waves and large storms, are likely to become more frequent or more intense. The Paris Agreement is a legally binding international treaty to reduce carbon emissions which led to keep the global temperature rise well below 2°C [1]. This target cannot be achieved without deeply decarbonized energy production which focuses on three main factors: 1) Reducing energy consumption through improved efficiency (optimize), 2) Shifting energy demand to electricity and away from combustion of fossil fuels (electrify everything), 3) Shifting entirely to zero-carbon technologies such as solar photovoltaic (PV), wind, hydrogen, etc. to generate electricity (decarbonize). According to the United States (U.S.) Environmental Protection Agency (EPA), the largest contributors to anthropogenic U.S. greenhouse gas emissions in the descending order are transportation (29%), electricity (25%), industry (23%), agriculture (10%), commercial (7%), and residential (6%) [2]. In our previous research, we focused on decarbonization pathways using utility solar, hydrogen consumption and production (electrolyzer, compressor, storage tank, and stationary fuel cells) for improved utilities operation without considering transportation sector demand [3]. Here the focus is on energy decarbonization for both power and hydrogen fueled transportation sector using hydrogen systems. To this end, a vertically integrated 33-node distribution network which is managed by distribution system operator (DSO) is optimized. The DSO manages all the assets in the network including natural gas power plants (combined cycle units and combustion turbine units), solar PV, distributed energy storage systems, hydrogen systems (including storage and refueling station) with the objective of cost minimization. Besides managing the aforementioned technologies, the DSO ensures safe and reliable operation of these assets by considering the technical and physical constraints of the network including voltage regulation, power flow and line congestion management, etc. The DSO in this study also manages the transportation sector hydrogen demand of fuel cell electric vehicles, medium- and heavy-duty vehicles. Simulation results show that hydrogen demand from the transportation sector is the main driver in sizing of hydrogen system components. Additionally, for the 33-node distribution network to fully supply heavy-duty vehicles demand, natural gas power plants must be operated to supply the required electricity for the electrolyzers to supply hydrogen for these vehicles. Furthermore, different sensitivity analysis for various PV and hydrogen demand penetrations will be presented.

In this issue of Journal of Disaster Research, we introduce nine papers on societal responses to recent catastrophic disasters with special focus on long-term recovery processes in Japan and the United States. As disaster impacts increase, we also find that recovery times take longer and the processes for recovery become more complicated. On January 17th of 1995, a magnitude 7.2 earthquake hit the Hanshin and Awaji regions of Japan, resulting in the largest disaster in Japan in 50 years. In this disaster which we call the Kobe earthquake hereafter, over 6,000 people were killed and the damage and losses totaled more than 100 billion US dollars. The long-term recovery from the Kobe earthquake disaster took more than ten years to complete. One of the most important responsibilities of disaster researchers has been to scientifically monitor and record the long-term recovery process following this unprecedented disaster and discern the lessons that can be applied to future disasters. The first seven papers in this issue present some of the key lessons our research team learned from the studying the long-term recovery following the Kobe earthquake disaster. We have two additional papers that deal with two recent disasters in the United States – the terrorist attacks on World Trade Center in New York on September 11 of 2001 and the devastation of New Orleans by the 2005 Hurricane Katrina and subsequent levee failures. These disasters have raised a number of new research questions about long-term recovery that US researchers are studying because of the unprecedented size and nature of these disasters’ impacts. Mr. Mammen’s paper reviews the long-term recovery processes observed at and around the World Trade Center site over the last six years. Ms. Johnson’s paper provides a detailed account of the protracted reconstruction planning efforts in the city of New Orleans to illustrate a set of sufficient and necessary conditions for successful recovery. All nine papers in this issue share a theoretical framework for long-term recovery processes which we developed based first upon the lessons learned from the Kobe earthquake and later expanded through observations made following other recent disasters in the world. The following sections provide a brief description of each paper as an introduction to this special issue. 1. The Need for Multiple Recovery Goals After the 1995 Kobe earthquake, the long-term recovery process began with the formulation of disaster recovery plans by the City of Kobe – the most severely impacted municipality – and an overarching plan by Hyogo Prefecture which coordinated 20 impacted municipalities; this planning effort took six months. Before the Kobe earthquake, as indicated in Mr. Maki’s paper in this issue, Japanese theories about, and approaches to, recovery focused mainly on physical recovery, particularly: the redevelopment plans for destroyed areas; the location and standards for housing and building reconstruction; and, the repair and rehabilitation of utility systems. But the lingering problems of some of the recent catastrophes in Japan and elsewhere indicate that there are multiple dimensions of recovery that must be considered. We propose that two other key dimensions are economic recovery and life recovery. The goal of economic recovery is the revitalization of the local disaster impacted economy, including both major industries and small businesses. The goal of life recovery is the restoration of the livelihoods of disaster victims. The recovery plans formulated following the 1995 Kobe earthquake, including the City of Kobe’s and Hyogo Prefecture’s plans, all stressed these two dimensions in addition to physical recovery. The basic structure of both the City of Kobe’s and Hyogo Prefecture’s recovery plans are summarized in Fig. 1. Each plan has three elements that work simultaneously. The first and most basic element of recovery is the restoration of damaged infrastructure. This helps both physical recovery and economic recovery. Once homes and work places are recovered, Life recovery of the impacted people can be achieved as the final goal of recovery. Figure 2 provides a “recovery report card” of the progress made by 2006 – 11 years into Kobe’s recovery. Infrastructure was restored in two years, which was probably the fastest infrastructure restoration ever, after such a major disaster; it astonished the world. Within five years, more than 140,000 housing units were constructed using a variety of financial means and ownership patterns, and exceeding the number of demolished housing units. Governments at all levels – municipal, prefectural, and national – provided affordable public rental apartments. Private developers, both local and national, also built condominiums and apartments. Disaster victims themselves also invested a lot to reconstruct their homes. Eleven major redevelopment projects were undertaken and all were completed in 10 years. In sum, the physical recovery following the 1995 Kobe earthquake was extensive and has been viewed as a major success. In contrast, economic recovery and life recovery are still underway more than 13 years later. Before the Kobe earthquake, Japan’s policy approaches to recovery assumed that economic recovery and life recovery would be achieved by infusing ample amounts of public funding for physical recovery into the disaster area. Even though the City of Kobe’s and Hyogo Prefecture’s recovery plans set economic recovery and life recovery as key goals, there was not clear policy guidance to accomplish them. Without a clear articulation of the desired end-state, economic recovery programs for both large and small businesses were ill-timed and ill-matched to the needs of these businesses trying to recover amidst a prolonged slump in the overall Japanese economy that began in 1997. “Life recovery” programs implemented as part of Kobe’s recovery were essentially social welfare programs for low-income and/or senior citizens. 2. Requirements for Successful Physical Recovery Why was the physical recovery following the 1995 Kobe earthquake so successful in terms of infrastructure restoration, the replacement of damaged housing units, and completion of urban redevelopment projects? There are at least three key success factors that can be applied to other disaster recovery efforts: 1) citizen participation in recovery planning efforts, 2) strong local leadership, and 3) the establishment of numerical targets for recovery. Citizen participation As pointed out in the three papers on recovery planning processes by Mr. Maki, Mr. Mammen, and Ms. Johnson, citizen participation is one of the indispensable factors for successful recovery plans. Thousands of citizens participated in planning workshops organized by America Speaks as part of both the World Trade Center and City of New Orleans recovery planning efforts. Although no such workshops were held as part of the City of Kobe’s recovery planning process, citizen participation had been part of the City of Kobe’s general plan update that had occurred shortly before the earthquake. The City of Kobe’s recovery plan is, in large part, an adaptation of the 1995-2005 general plan. On January 13 of 1995, the City of Kobe formally approved its new, 1995-2005 general plan which had been developed over the course of three years with full of citizen participation. City officials, responsible for drafting the City of Kobe’s recovery plan, have later admitted that they were able to prepare the city’s recovery plan in six months because they had the preceding three years of planning for the new general plan with citizen participation. Based on this lesson, Odiya City compiled its recovery plan based on the recommendations obtained from a series of five stakeholder workshops after the 2004 Niigata Chuetsu earthquake. <strong>Fig. 1. </strong> Basic structure of recovery plans from the 1995 Kobe earthquake. <strong>Fig. 2. </strong> “Disaster recovery report card” of the progress made by 2006. Strong leadership In the aftermath of the Kobe earthquake, local leadership had a defining role in the recovery process. Kobe’s former Mayor, Mr. Yukitoshi Sasayama, was hired to work in Kobe City government as an urban planner, rebuilding Kobe following World War II. He knew the city intimately. When he saw damage in one area on his way to the City Hall right after the earthquake, he knew what levels of damage to expect in other parts of the city. It was he who called for the two-month moratorium on rebuilding in Kobe city on the day of the earthquake. The moratorium provided time for the city to formulate a vision and policies to guide the various levels of government, private investors, and residents in rebuilding. It was a quite unpopular policy when Mayor Sasayama announced it. Citizens expected the city to be focusing on shelters and mass care, not a ban on reconstruction. Based on his experience in rebuilding Kobe following WWII, he was determined not to allow haphazard reconstruction in the city. It took several years before Kobe citizens appreciated the moratorium. Numerical targets Former Governor Mr. Toshitami Kaihara provided some key numerical targets for recovery which were announced in the prefecture and municipal recovery plans. They were: 1) Hyogo Prefecture would rebuild all the damaged housing units in three years, 2) all the temporary housing would be removed within five years, and 3) physical recovery would be completed in ten years. All of these numerical targets were achieved. Having numerical targets was critical to directing and motivating all the stakeholders including the national government’s investment, and it proved to be the foundation for Japan’s fundamental approach to recovery following the 1995 earthquake. 3. Economic Recovery as the Prime Goal of Disaster Recovery In Japan, it is the responsibility of the national government to supply the financial support to restore damaged infrastructure and public facilities in the impacted area as soon as possible. The long-term recovery following the Kobe earthquake is the first time, in Japan’s modern history, that a major rebuilding effort occurred during a time when there was not also strong national economic growth. In contrast, between 1945 and 1990, Japan enjoyed a high level of national economic growth which helped facilitate the recoveries following WWII and other large fires. In the first year after the Kobe earthquake, Japan’s national government invested more than US$ 80 billion in recovery. These funds went mainly towards the repair and reconstruction of infrastructure and public facilities. Now, looking back, we can also see that these investments also nearly crushed the local economy. Too much money flowed into the local economy over too short a period of time and it also did not have the “trickle-down” effect that might have been intended. To accomplish numerical targets for physical recovery, the national government awarded contracts to large companies from Osaka and Tokyo. But, these large out-of-town contractors also tended to have their own labor and supply chains already intact, and did not use local resources and labor, as might have been expected. Essentially, ten years of housing supply was completed in less than three years, which led to a significant local economic slump. Large amounts of public investment for recovery are not necessarily a panacea for local businesses, and local economic recovery, as shown in the following two examples from the Kobe earthquake. A significant national investment was made to rebuild the Port of Kobe to a higher seismic standard, but both its foreign export and import trade never recovered to pre-disaster levels. While the Kobe Port was out of business, both the Yokohama Port and the Osaka Port increased their business, even though many economists initially predicted that the Kaohsiung Port in Chinese Taipei or the Pusan Port in Korea would capture this business. Business stayed at all of these ports even after the reopening of the Kobe Port. Similarly, the Hanshin Railway was severely damaged and it took half a year to resume its operation, but it never regained its pre-disaster readership. In this case, two other local railway services, the JR and Hankyu lines, maintained their increased readership even after the Hanshin railway resumed operation. As illustrated by these examples, pre-disaster customers who relied on previous economic output could not necessarily afford to wait for local industries to recover and may have had to take their business elsewhere. Our research suggests that the significant recovery investment made by Japan’s national government may have been a disincentive for new economic development in the impacted area. Government may have been the only significant financial risk-taker in the impacted area during the national economic slow-down. But, its focus was on restoring what had been lost rather than promoting new or emerging economic development. Thus, there may have been a missed opportunity to provide incentives or put pressure on major businesses and industries to develop new businesses and attract new customers in return for the public investment. The significant recovery investment by Japan’s national government may have also created an over-reliance of individuals on public spending and government support. As indicated in Ms. Karatani’s paper, individual savings of Kobe’s residents has continued to rise since the earthquake and the number of individuals on social welfare has also decreased below pre-disaster levels. Based on our research on economic recovery from the Kobe earthquake, at least two lessons emerge: 1) Successful economic recovery requires coordination among all three recovery goals – Economic, Physical and Life Recovery, and 2) “Recovery indices” are needed to better chart recovery progress in real-time and help ensure that the recovery investments are being used effectively. Economic recovery as the prime goal of recovery Physical recovery, especially the restoration of infrastructure and public facilities, may be the most direct and socially accepted provision of outside financial assistance into an impacted area. However, lessons learned from the Kobe earthquake suggest that the sheer amount of such assistance may not be effective as it should be. Thus, as shown in Fig. 3, economic recovery should be the top priority goal for recovery among the three goals and serve as a guiding force for physical recovery and life recovery. Physical recovery can be a powerful facilitator of post-disaster economic development by upgrading social infrastructure and public facilities in compliance with economic recovery plans. In this way, it is possible to turn a disaster into an opportunity for future sustainable development. Life recovery may also be achieved with a healthy economic recovery that increases tax revenue in the impacted area. In order to achieve this coordination among all three recovery goals, municipalities in the impacted areas should have access to flexible forms of post-disaster financing. The community development block grant program that has been used after several large disasters in the United States, provide impacted municipalities with a more flexible form of funding and the ability to better determine what to do and when. The participation of key stakeholders is also an indispensable element of success that enables block grant programs to transform local needs into concrete businesses. In sum, an effective economic recovery combines good coordination of national support to restore infrastructure and public facilities and local initiatives that promote community recovery. Developing Recovery Indices Long-term recovery takes time. As Mr. Tatsuki’s paper explains, periodical social survey data indicates that it took ten years before the initial impacts of the Kobe earthquake were no longer affecting the well-being of disaster victims and the recovery was completed. In order to manage this long-term recovery process effectively, it is important to have some indices to visualize the recovery processes. In this issue, three papers by Mr. Takashima, Ms. Karatani, and Mr. Kimura define three different kinds of recovery indices that can be used to continually monitor the progress of the recovery. Mr. Takashima focuses on electric power consumption in the impacted area as an index for impact and recovery. Chronological change in electric power consumption can be obtained from the monthly reports of power company branches. Daily estimates can also be made by tracking changes in city lights using a satellite called DMSP. Changes in city lights can be a very useful recovery measure especially at the early stages since it can be updated daily for anywhere in the world. Ms. Karatani focuses on the chronological patterns of monthly macro-statistics that prefecture and city governments collect as part of their routine monitoring of services and operations. For researchers, it is extremely costly and virtually impossible to launch post-disaster projects that collect recovery data continuously for ten years. It is more practical for researchers to utilize data that is already being collected by local governments or other agencies and use this data to create disaster impact and recovery indices. Ms. Karatani found three basic patterns of disaster impact and recovery in the local government data that she studied: 1) Some activities increased soon after the disaster event and then slumped, such as housing construction; 2) Some activities reduced sharply for a period of time after the disaster and then rebounded to previous levels, such as grocery consumption; and 3) Some activities reduced sharply for a while and never returned to previous levels, such as the Kobe Port and Hanshin Railway. Mr. Kimura focuses on the psychology of disaster victims. He developed a “recovery and reconstruction calendar” that clarifies the process that disaster victims undergo in rebuilding their shattered lives. His work is based on the results of random surveys. Despite differences in disaster size and locality, survey data from the 1995 Kobe earthquake and the 2004 Niigata-ken Chuetsu earthquake indicate that the recovery and reconstruction calendar is highly reliable and stable in clarifying the recovery and reconstruction process. <strong>Fig. 3.</strong> Integrated plan of disaster recovery. 4. Life Recovery as the Ultimate Goal of Disaster Recovery Life recovery starts with the identification of the disaster victims. In Japan, local governments in the impacted area issue a “damage certificate” to disaster victims by household, recording the extent of each victim’s housing damage. After the Kobe earthquake, a total of 500,000 certificates were issued. These certificates, in turn, were used by both public and private organizations to determine victim’s eligibility for individual assistance programs. However, about 30% of those victims who received certificates after the Kobe earthquake were dissatisfied with the results of assessment. This caused long and severe disputes for more than three years. Based on the lessons learned from the Kobe earthquake, Mr. Horie’s paper presents (1) a standardized procedure for building damage assessment and (2) an inspector training system. This system has been adopted as the official building damage assessment system for issuing damage certificates to victims of the 2004 Niigata-ken Chuetsu earthquake, the 2007 Noto-Peninsula earthquake, and the 2007 Niigata-ken Chuetsu Oki earthquake. Personal and family recovery, which we term life recovery, was one of the explicit goals of the recovery plan from the Kobe earthquake, but it was unclear in both recovery theory and practice as to how this would be measured and accomplished. Now, after studying the recovery in Kobe and other regions, Ms. Tamura’s paper proposes that there are seven elements that define the meaning of life recovery for disaster victims. She recently tested this model in a workshop with Kobe disaster victims. The seven elements and victims’ rankings are shown in Fig. 4. Regaining housing and restoring social networks were, by far, the top recovery indicators for victims. Restoration of neighborhood character ranked third. Demographic shifts and redevelopment plans implemented following the Kobe earthquake forced significant neighborhood changes upon many victims. Next in line were: having a sense of being better prepared and reducing their vulnerability to future disasters; regaining their physical and mental health; and restoration of their income, job, and the economy. The provision of government assistance also provided victims with a sense of life recovery. Mr. Tatsuki’s paper summarizes the results of four random-sample surveys of residents within the most severely impacted areas of Hyogo Prefecture. These surveys were conducted biannually since 1999,. Based on the results of survey data from 1999, 2001, 2003, and 2005, it is our conclusion that life recovery took ten years for victims in the area impacted significantly by the Kobe earthquake. Fig. 5 shows that by comparing the two structural equation models of disaster recovery (from 2003 and 2005), damage caused by the Kobe earthquake was no longer a determinant of life recovery in the 2005 model. It was still one of the major determinants in the 2003 model as it was in 1999 and 2001. This is the first time in the history of disaster research that the entire recovery process has been scientifically described. It can be utilized as a resource and provide benchmarks for monitoring the recovery from future disasters. <strong>Fig. 4.</strong> Ethnographical meaning of “life recovery” obtained from the 5th year review of the Kobe earthquake by the City of Kobe. <strong>Fig. 5.</strong> Life recovery models of 2003 and 2005. 6. The Need for an Integrated Recovery Plan The recovery lessons from Kobe and other regions suggest that we need more integrated recovery plans that use physical recovery as a tool for economic recovery, which in turn helps disaster victims. Furthermore, we believe that economic recovery should be the top priority for recovery, and physical recovery should be regarded as a tool for stimulating economic recovery and upgrading social infrastructure (as shown in Fig. 6). With this approach, disaster recovery can help build the foundation for a long-lasting and sustainable community. Figure 6 proposes a more detailed model for a more holistic recovery process. The ultimate goal of any recovery process should be achieving life recovery for all disaster victims. We believe that to get there, both direct and indirect approaches must be taken. Direct approaches include: the provision of funds and goods for victims, for physical and mental health care, and for housing reconstruction. Indirect approaches for life recovery are those which facilitate economic recovery, which also has both direct and indirect approaches. Direct approaches to economic recovery include: subsidies, loans, and tax exemptions. Indirect approaches to economic recovery include, most significantly, the direct projects to restore infrastructure and public buildings. More subtle approaches include: setting new regulations or deregulations, providing technical support, and creating new businesses. A holistic recovery process needs to strategically combine all of these approaches, and there must be collaborative implementation by all the key stakeholders, including local governments, non-profit and non-governmental organizations (NPOs and NGOs), community-based organizations (CBOs), and the private sector. Therefore, community and stakeholder participation in the planning process is essential to achieve buy-in for the vision and desired outcomes of the recovery plan. Securing the required financial resources is also critical to successful implementation. In thinking of stakeholders, it is important to differentiate between supporting entities and operating agencies. Supporting entities are those organizations that supply the necessary funding for recovery. Both Japan’s national government and the federal government in the U.S. are the prime supporting entities in the recovery from the 1995 Kobe earthquake and the 2001 World Trade Center recovery. In Taiwan, the Buddhist organization and the national government of Taiwan were major supporting entities in the recovery from the 1999 Chi-Chi earthquake. Operating agencies are those organizations that implement various recovery measures. In Japan, local governments in the impacted area are operating agencies, while the national government is a supporting entity. In the United States, community development block grants provide an opportunity for many operating agencies to implement various recovery measures. As Mr. Mammen’ paper describes, many NPOs, NGOs, and/or CBOs in addition to local governments have had major roles in implementing various kinds programs funded by block grants as part of the World Trade Center recovery. No one, single organization can provide effective help for all kinds of disaster victims individually or collectively. The needs of disaster victims may be conflicting with each other because of their diversity. Their divergent needs can be successfully met by the diversity of operating agencies that have responsibility for implementing recovery measures. In a similar context, block grants made to individual households, such as microfinance, has been a vital recovery mechanism for victims in Thailand who suffered from the 2004 Sumatra earthquake and tsunami disaster. Both disaster victims and government officers at all levels strongly supported the microfinance so that disaster victims themselves would become operating agencies for recovery. Empowering individuals in sustainable life recovery is indeed the ultimate goal of recovery. <strong>Fig. 6.</strong> A holistic recovery policy model.

The work begins with the following question: why study and analyze the segment da public ad-ministration, in particular public buildings? If in the end, segments: industrial, commercial, and residential, are the major consumers of the electricity supply and the graph the Figure 1 shows the evolution of electricity consumption in the industrial segment in the period between 1998 and 2010. The answer to that question requires in a way o understanding about discipline of the public manager and legislative acts.The public segment is directly linked to the executive, which through legislative acts can issue Decrees, Norms, Infra-Standards and Regulations that affect directly the public manager and consequently the units of public buildings, with this the answers to the changes, are of habits and customs or adaptation to the conditions that prevail at the moment, are more effective and allow us to measure with greater precision, these actions are reapplicable in the spheres, federal, state and municipal levels. The other segments only change their working conditions in the routine of the increase in the amounts charged in the tariffs and even then instead of implementing immediately measures to reduce spending prefer to pass on in some way the increase in tariffs, in addition to of this, the residential consumer does not usu-ally have the immediate sensitivity of the needs of changes habits because they are immediately impacted by increases in in the values of tariffs, and they think you already make the contributi-on by paying for the raise and often find that reducing costs is continue paying the same amount as the bill and don't know that that's an illusion making your perception disorderly. After a cer-tain time from delay all end up surrendering the reduction of spending on the electricity supply, returning the accounts to levels stipulated by the government measures as can be seen in the graph da Figure 1, that the effects of 2001 only impact more intensely on the industry in 2004. One of the objectives of this work is subsidize the Society in general and decision makers at various levels of public administration, with information, concepts, and suggestions for the de-velopment of actions aimed at reducing spending on the electricity supply, without prejudice to the environment. The work is based on the study impacts on the consumption, in demand, in number of units the public buildings and investments of the energy efficiency actions, in period understood between 1998 e 2010, in Brazil, but than addresses also from 1984 the effects cau-sed by rationings electric power, as crisiss in the financial system, legislative changes (Consuption) and elections (changes in guidelines of the policies of insvestiments). After constitutional reform, 1988, some functions that belonged to the passed to the municipality, how for example, the issue of public health, and the public administration had to adapt in a short time to its new role next to society, but these changes have not reduced the number of public buildings, such as education, where many buildings Were Sha-red between the state and the municipality, and in the following years it become imperative to increase the number of administrative units to meet the new functions of public administration and the increase in the economically active population. The issues relating to conservation of energy only begun to be developed with greater depth after 1998, in special with the lesson le-arned after the rationing of 2001, when many public managers have begun to consider reducing electricity consumption for avoid a uncomfortable situation penalties depending on the legislati-ve measures applied at the time. But it was found that from 2003, the effects of electricity ratio-ning have been dissipated and the escalation to increase consumption returned similar to what had occurred before 2001. One of the factors that corroborates the above is explained in the growth of eletricity consumption by the government, represented in the graph from figure 2. This information is also analyzed from the point of view of the regions of Brazil (north, south, center-west, and southeast). With the result of the application of this work we can induce an administrative vision focused on efficiency generating the reduction of spending on the electri-city supply in the public administration [5], we can show for society in general and to public managers, such as the behavior of the electricity supply in the public administration segment, through the studies of evolution the number of units of public buildings, the consumption and demand of electricity, when through: financial crises, electric power rationing and changes in these investment policies (Elections). In addition, to show the actions implemented aimed at the search for solutions and among them we can mention legislative measures, implementation of decrees aimed at reducing spending on electricity, such as the maintenance of internal energy conservation commissions, CICE, techno-logical update of equipment performing a pre-diagnosis [3], and the energy saved in the public sector between 2008 and 2018. This work has as relevance the analysis of a long information period and many of them in the past had periodicity of up to five years, and one of the possibilities in applications is the deter-mination of indexes for reference of expenses with the electric energy, as for example we can mention: consumption of electricity per employee public of administrative units, consumption per square meter of public administrative unit, and also from the point of view of electricity de-mand, in addition serves as orientative in the search for solutions for managers of public admi-nistration in the actions taken in the uncomfortable periods of the economy and rationing of electricity energy. Currently we are going through new challenges determined after the year 2020, which are: pan-demic infection through the COVID-19 virus, which generated the lockdown of minimally one year and which still persists the fighting in the year 2022; the change in the load profile as a function of the reduction of consumption and demand, with a view to the closure of the publics units; the Russia-Ukraine war that started oil scarcity, rising food prices, severe uncertainty in the international financial market due to the possibility of non-compliance with Russia's payments, increased inflation in the United States, Elections that generate changes or policy maintenance of the government in Brazil and the World. The only certainty we have at this moment is that there is still no forecast of the impacts, conse-quences, and the end of these effects. In the development of this work, a necessary time was allocated to the compilation and modeling of the data obtained, software development, interac-tion, and partnership with The National Eletric Energy Agency in data acquisition, web search of information, for example at the Brazilian Institute of Geography and Statistics and United Nati-ons Development Program.

The global economy lives a period of great apprehension regarding energy issues for the near future. The developed countries continue to have a steady growth and the emerging countries increase their rates of economic growth, which causes a continuing increase in global energy demand. In that context, RETERM calls the attention of the readers to the growing need for the search of alternative energy sources to the current oil based global economy. This effort certainly involves focus and work of the energy related scientific community, field in which Thermal Engineering is part of. There are several alternatives currently under consideration. The Department of Energy of the United States of America is currently seriously focused on research and development of alternative energy sources, mainly aiming the independence of external suppliers, such as nuclear energy and the hydrogen economy. Therefore, it is vital to any nation the allocation of financial resources to the development and technological control of new processes that lead to self-sufficiency in energy generation for internal consumption and economic growth. The fuel cell systems will be of critical importance in the possible hydrogen economy scenery for the near future, and they are at the forefront of the emerging technologies for electric power generation for stationary, mobile and portable applications. The high energy conversion efficiencies, extremely low onsite environmental pollution and noise are among their major advantages in comparison to other systems. However, there are major technological and economic hurdles to be overcome prior to their large scale practical implementation. Similarly to nuclear technology, in fuel cell technology it is observed that each research group develops its own know-how independently and with little exchange of information.In this fifth number, we continue to publish the best articles written in English language, presented at the 9th Brazilian Congress of Thermal Engineering and Sciences, ENCIT 2002, held in Caxambu, MG, from October 15th to 18th, 2002, that were selected by the Associate Technical Editors of RETERM, according to the scientific criteria of the journal. The idea is to increase the industrial and scientific impact of the research results presented in the Congress. Additional articles that have been submitted and reviewed in the regular RETERM publication process are also included in the current issue.

The manufacturing process for new passenger vehicles is based not only on their design and manufacture, but also on validation and testing, especially in the area of exhaust emissions. The car manufacturer is obliged to approve the type of each new model in accordance with the regulations. The regulation associated with the relevant directive includes a number of requirements, including the emissions of pollutants in the exhaust gas, which are imposed on newly manufactured vehicles. Along with the development of the automotive industry, more and more attention has been paid to the pollution that forms in the internal combustion engines of vehicles. The European Union has introduced standards known as “EURO” to define emission limits for the main pollutants in exhaust gases. The tests are carried out for all passenger cars in the same way: on a dynamometer, in a climatic chamber (with the possibility of temperature adjustment) and in accordance with a certain driving cycle. Road tests are designed to check fuel consumption and exhaust emissions. In September 2017, a new procedure was introduced called the World Harmonized Light Vehicle Test Procedure (WLTP), which includes several driving cycles called WLTC. The introduction of the new test was driven by the very dynamic development in the automotive industry of hybrid and electric vehicles. The previous NEDC test did not take into account several important parameters such as motor power or drive type. Due to the different specifics of road traffic in the United States, their own road tests were developed, in contrast to European ones. Tests are conducted in accordance with FTP-75 (Federal Testing Procedure). The test parameters take into account driving stability and engine operating conditions, on which the values of pollutant emissions in the exhaust gases depend. Due to the difference in laboratory driving cycles, according to traffic conditions, the values of pollutant emissions in the exhaust gases during road tests differ from those provided by the manufacturers. The article compares the characteristic test parameters according to WLTC, NEDC, American FTP-75 cycles (with additions SC03 and US06) and own road driving cycle in the Rzeszow region. Based on the analysis carried out, it was established that laboratory tests will never 100% reflect those driving conditions and driving on the road. However, the WLTC test has the advantage of being more realistic. Its high average ride speeds, longer stops, long distance traveled and higher top speed are more realistic than the NEDC test. KEY WORDS: VEHICLE TESTING, EFFECTIVE Emissions, WORLD HARMONIZED PASSENGER VEHICLE TEST PROCEDURE, NEW EUROPEAN DRIVING CYCLE, FEDERAL TESTING PROCEDURE.

The solutions are there to improve energy efficiency in all sectors. We don’t need to wait. We need action because the greenest energy is the energy we don’t use.—Kim Fausing, president and CEO, Danfoss I don’t know any other solution like energy efficiency that can simultaneously address our economic crisis, energy crisis, and climate crisis. … efficiency is the very first fuel.—Fatih Birol, executive director of the IEA Energy security, energy prices, and the cost of living were the focus of the International Energy Agency’s (IEA) 7th Annual Global Conference on Energy Efficiency held last month in Sønderborg, Denmark. Twenty-four governments from around the world issued a joint statement stressing the importance of energy efficiency to address the energy crisis, rising inflation, and greenhouse-gas emissions. IEA said, “It was the first event of its kind at which so many governments—including France, Germany, Indonesia, Japan, Mexico, Senegal, and the United States—have made a specific call for stronger action on energy efficiency.” Although energy efficiency has long been mentioned as a means toward achieving Paris Agreement goals, the “more exciting” announcements such as carbon capture and storage grabbed attention. Big projects offering big solutions proposed by big corporations and governments outshone energy efficiency, which also called for personal actions. Although that may be an unpleasant truth, more attention may be turning to it. Global energy prices are high and volatile—an unsettling mix—and are hitting hard the wallets of individuals, households, industries, and entire economies. Gasoline, other fuels, and electricity prices are high (and likely to get higher), and added to inflation, are increasing the cost of most everything else. A threshold has been met or surpassed for what is considered “affordable,” and the critical role of demand-side actions, including energy efficiency, should gain traction. Consider this: Doubling the current rate of improvement in energy intensity, a measure of the economy’s energy efficiency, from 2 to 4% per year over this decade could potentially avoid 95 exajoule (EJ) per year of final energy consumption—equivalent to China’s current final energy consumption. (One exajoule is equal to 1018 joules.) IEA added that with each unit of energy delivering more than it does today, final energy demand by 2030 could be 5% lower yet serve an economy 40% larger. With an eye toward energy security, cutting 95 EJ per year by 2030 avoids 30 million BOPD, approximately triple Russia’s average 2021 production. And it avoids 650 billion m3 of natural gas, around four times the EU’s imports from Russia in 2021.Also highlighted was a shift in global efforts to provide clean and efficient cooking and hearting to all who lack it today. More than 20 EJ demand would be reduced for the traditional use of biomass such as wood and charcoal in 2030, dramatically improving the lives of billions of people. For example, household air pollution is linked to around 2.5 million premature deaths a year, with women and children most affected. One-third of the decreased energy demand is forecast to come from the use of more technically efficient equipment ranging from air conditioners to vehicles, including the adoption of electric vehicles. Electrification provides around 20%, for example through switching to electricity for low-temperature heat in industry and replacing fossil-fuel boilers with more-efficient heat pumps. Smart (digital) controls and an increase in recycling of plastics and scrap steel account for another 30% of the decrease in demand. Changes in human behavior, our personal actions, could cut 18% or so through changing travel patterns and turning down thermostats. Buildings, heavy industry, manufacturing, and the transport sector hold significant potential for reducing energy use. In 2020 transportation’s energy consumption totaled around 105 EJ and accounted for around 27% of total global energy-related emissions, according to IEA. Buildings accounted for 129 EJ and contributed 28%, and industrial energy consumption was 156 EJ and contributed 39% of the emissions. Although IEA said the oil market may rebalance in the second half of this year as oil demand is tempered, OPEC+ increases supply, and strategic reserves are released, it added, “This situation might prove short-lived. OPEC+ capacity constraints set the stage for 2023, when global oil supply will struggle to keep pace with demand. While non-OPEC+ continues to power ahead, OPEC+ would have to further deflate its shrunken capacity cushion to keep the implied balance from tipping into deficit.”

AbstractThe purpose of this paper is twofold: analyzing stationarity of energy consumption by source in the United States and studying their cycles and pairwise synchronization. We study a panel of nine time series of monthly energy consumption for the period 1973–2022. Four of the series (namely coal, natural gas, petroleum, and nuclear electric power consumption) are non-renewables, whereas the remaining ones (hydroelectric power, geothermal, biomass, solar, and wind energy consumption) are renewable energy sources. We employ a nonparametric, panel stationarity testing approach. The results indicate that most of the series may be trend-stationarity, with nuclear and geothermal energy consumption being the only exceptions. Additionally, a study on potential cycles in the series of energy consumption by source is carried out, and subsequently we analyze pairwise concordance between states of different energy sources and between states of energy sources and the business cycle. Significant correlations are detected in the latter analysis, which are positive in the case of fossil fuel sources and negative for two renewable sources, namely geothermal and biomass energy consumption.

AbstractWeather is a key driving factor of power outages. In this article, a methodology to forecast weather‐related power distribution outages one day‐ahead on an hourly basis is presented. A solution to address the data imbalance issue is proposed, where only a small portion of the data represents the hours impacted by outages, in the form of a weighted logistic regression model. Data imbalance is a key modelling challenge for small and rural electric utilities. The weights for outage and non‐outage hours are determined by the reciprocals of their corresponding number of hours. To demonstrate the effectiveness of the proposed model, two case studies using data from a small electric utility company in the United States are presented. One case study analyses the weather‐related outages aggregated up to the city level. The other case study is based on the distribution substation level, which has rarely been tackled in the outage prediction literature. Compared with two variants of ordinary logistic regression with equal weights, the proposed model shows superior performance in terms of geometric mean.

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8 kW or lower for Sweden and at 2.1 kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation.

Abstract Water consumed by power plants is transferred virtually from producers to consumers on the electric grid. This network of virtual transfers varies spatially and temporally on a sub-annual scale. In this study, we focused on cooling water consumed by thermoelectric power plants and water evaporated from hydropower reservoirs. We analyzed blue and grey virtual water flows between balancing authorities in the United States electric grid from 2016 to 2021. Transfers were calculated using thermoelectric water consumption volumes reported in Form EIA-923, power plant data from Form EIA-860, water consumption factors from literature, and electricity transfer data from Form EIA-930. The results indicate that virtual water transfers follow seasonal trends. Virtual blue water transfers are dominated by evaporation from hydropower reservoirs in high evaporation regions and peak around November. Virtual grey water transfers reach a maximum peak during the summer months and a smaller peak during the winter. Notable virtual blue water transfers occur between Arizona and California as well as surrounding regions in the Southwest. Virtual grey water transfers are greatest in the Eastern United States where older, once-through cooling systems are still in operation. Understanding the spatial and temporal transfer of water resources has important policy, water management, and equity implications for understanding burden shifts between regions.

Abstract Solar-powered organic Rankine cycle (ORC) is considered a promising technology and has the potential to provide clean electric energy. Extensive studies on the design of ORC systems have been conducted and reported in the literature. However, few studies have presented the influence of climate zones on the performance of a solar-powered ORC, especially for an integrated ORC and energy storage system. This paper presents an analysis to determine the performance of solar-powered ORCs with electric energy storage (EES) systems to supply electricity to buildings in different climate zones in the United States. The building type evaluated in this paper is a large office, and the energy consumption of the facility in each climate location was determined using EnergyPlus. The ORC-EES operational strategy used in this investigation is described as follows: when solar irradiation is adequate to produce power, the ORC charges the EES. Then, when there is no solar energy available, the EES provides power to the building. The ORC-EES is evaluated based on the potential to reduce the operational cost, the primary energy consumption, and the carbon dioxide emission. Furthermore, the influence of the number of solar collectors and the EES size on the performance of the ORC-EES system is investigated.

In the movie "Apollo 13" the engineers had a discussion on howto get the astronauts home safely. A number of suggestions were madeand they began to debate the options. One engineer stopped the conver-sation, and said: "Power is everything."None of the options was possible without power.Our energy resources are indeed finite, on a much larger scale ofcourse, but they must be managed. Today, more than 40% of the earth'sfive billion people are not connected to electric power grids. Althoughthe United States' more than 250 million citizens, along with other in-dustrialized nations , are the largest consumers of electricity, ourconsumption grows by only 1.4% per year. The rest of the world is in-creasing consumption by 2.5% per year.The US Department of Energy has estimated that by the year 2015,the consumption of electricity in developing countries (80% of theworld's population) will equal that of industrialized nations. Buildingmore power plants that burn nonrenewable fuels has environmentalimpacts and nuclear energy receives significant oppos ition from thegeneral public. No one energy source will satisfy this demand, so Iwould like to introduce to you an option that to the best of my knowl-edge has not been discussed in this or any AEE publication, that is SpaceSolar Power (SSP).

The relationship between economic growth and CO2 emission has been a widely researched topic in the global context. This paper intends to shed light on the relationship between economic growth and CO2 emission in selected group of countries with special comparison between Sri Lanka and each country since it is the research gap identified by the researchers. The objective of this paper is to investigate the relationship between economic growth and CO2 emission in Sri Lanka in comparison with the reference countries. Examining the impact of the size of urban/rural population, GDP growth rate and electric power consumption on CO2 emission are specific objectives. The group of countries selected for this study are Sri Lanka, India, Norway, and the United States of America. This study uses secondary data for each country collected from the World Bank database for the period of 1994-2018. A period of 25 years with 20 cross sections draws 500 total observations. The Fixed Effect Panel Regression (FEPR) method is applied by using SPSS software as the analytical tool of the study. As expected, the p values of the study showed that there is a high impact of economic growth on CO2 emission in Sri Lanka and USA. Further, the study found that the economic growth of India and Norway have less effect on CO2 emission compared to Sri Lanka and USA. The study also found that the size of urban and rural population has a direct impact on CO2 emission in selected countries while GDP growth rate and electric power consumption has no impact into CO2 emission. The study concludes that the overall population of selected countries has significant impact on CO2 emissions since the increase in both urban and rural population has led to higher CO2 emission. Further, the research confirms the Environmental Kuznets Curve analysis since the results show that the countries with higher GDP growth rate and electric power consumption are contributing to lower CO2 emission. Finally, the research suggests that the Sri Lankan policy makers to implement a suitable program to lower the environmental degradation within the country. Developing or importing appropriate and advanced technology can minimize the environmental impact on developing process. Keywords: Economic growth, CO2 emission, Urban/rural population, GDP growth rate, Electric power consumption

Introduction: In the field of power systems, power load type prediction is a crucial task. Different types of loads, such as domestic, industrial, commercial, etc., have different energy consumption patterns. Therefore, accurate prediction of load types can help the power system better plan power supply strategies to improve energy utilization and stability. However, this task faces multiple challenges, including the complex topology of the power system, the diversity of time series data, and the correlation between data. With the rapid development of deep learning methods, researchers are beginning to leverage these powerful techniques to address this challenge. This study aims to explore how to optimize deep learning models to improve the accuracy of load type prediction and provide support for efficient energy management and optimization of smart grids.Methods: In this study, we propose a deep learning method that combines graph convolutional networks (GCN) and sequence-to-sequence (Seq2Seq) models and introduces an attention mechanism. The methodology involves multiple steps: first, we use the GCN encoder to process the topological structure information of the power system and encode node features into a graph data representation. Next, the Seq2Seq decoder takes the historical time series data as the input sequence and generates a prediction sequence of the load type. We then introduced an attention mechanism, which allows the model to dynamically adjust its attention to input data and better capture the relationship between time series data and graph data.Results: We conducted extensive experimental validation on four different datasets, including the National Grid Electricity Load Dataset, the Canadian Electricity Load Dataset, the United States Electricity Load Dataset, and the International Electricity Load Dataset. Experimental results show that our method achieves significant improvements in load type prediction tasks. It exhibits higher accuracy and robustness compared to traditional methods and single deep learning models. Our approach demonstrates advantages in improving load type prediction accuracy, providing strong support for the future development of the power system.Discussion: The results of our study highlight the potential of deep learning techniques, specifically the combination of GCN and Seq2Seq models with attention mechanisms, in addressing the challenges of load type prediction in power systems. By improving prediction accuracy and robustness, our approach can contribute to more efficient energy management and the optimization of smart grids.