Journal articles on the topic 'Electric power consumption – econometric models'
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Karpenko, S. M., N. V. Karpenko, and G. Y. Bezginov. "Forecasting of power consumption at mining enterprises using statistical methods." Mining Industry Journal (Gornay Promishlennost), no. 1/2022 (March 15, 2022): 82–88. http://dx.doi.org/10.30686/1609-9192-2022-1-82-88.
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Forecasting of electric power consumption with due account of assessed impact of various factors helps to make efficient technical and managerial decisions to optimize the electric power consumption processes, including preparation of bids for the wholesale electric power and capacity market. The article uses multivariate methods of statistical analysis and econometric methods based on time series analysis for model designing. The paper presents the results of developing the following models: a multifactor model of electrical power consumption using the regression analysis, the Principal Component Method with the assessment of the impact of production factors on electrical power consumption using elasticity coefficients, as well as the energy saving factor based on a variable structure model; trend additive and multiplicative forecast models of electrical consumption that take into account the seasonality factor, models with a change in trends, a linear dynamic model of electrical power consumption that takes into account the production output; a forecast adaptive polynomial model of electrical power consumption as well as the Winters model. The developed forecast models have a sufficiently high accuracy (accuracy of the MAPE was below 7%). The choice of the model type to forecast the electrical power consumption depends on the quantitative and qualitative characteristics of the time series, the structural relation between the series, the purpose and objectives of the modeling. In order to enhance the accuracy of the forecast it is required to regularly refine the model and adjust it to the actual situation with the due account of new factors and production trends while building different versions of scenarios and combined forecast models of electrical power consumption
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Dieudonné, Nzoko Tayo, Talla Konchou Franck Armel, Aloyem Kaze Claude Vidal, and Tchinda René. "Prediction of electrical energy consumption in Cameroon through econometric models." Electric Power Systems Research 210 (September 2022): 108102. http://dx.doi.org/10.1016/j.epsr.2022.108102.
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Shin, Sun-Youn, and Han-Gyun Woo. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms." Energies 15, no. 13 (July 2, 2022): 4880. http://dx.doi.org/10.3390/en15134880.
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In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data.
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Chudy-Laskowska, Katarzyna, and Tomasz Pisula. "Forecasting Household Energy Consumption in European Union Countries: An Econometric Modelling Approach." Energies 16, no. 14 (July 23, 2023): 5561. http://dx.doi.org/10.3390/en16145561.
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The article raises issues regarding the consumption of energy from both fossil and renewable sources in households. The research was carried out on the basis of data obtained from the Eurostat database, which covered the period from 1995 to 2021 and concerned the European Union countries. Increasing energy consumption and, thus, increasing household expenses affect their standard of living. The purpose of the analysis was to construct two econometric models for electricity consumption. The first model referred to the consumption of energy from fossil sources and the second from renewable sources. A forecast of energy consumption in households was also constructed on the basis of estimated models. Econometric modelling methods (multiple regression) and time-series forecasting methods (linear regression method, exponential smoothing models) were applied for the study. Research shows that the main factor that models energy consumption in households, both from fossil and renewable sources, is the final consumption expenditure of households (Euro per capita). The set of indicators for the models varies depending on the type of energy source. The forecast shows that the share of energy consumption obtained from fossil sources will decrease systematically, while the share of energy consumption from renewable sources will continue to increase systematically.
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Anh, Le Hoang, Gwang Hyun Yu, Dang Thanh Vu, Jin Sul Kim, Jung Il Lee, Jun Churl Yoon, and Jin Young Kim. "Stride-TCN for Energy Consumption Forecasting and Its Optimization." Applied Sciences 12, no. 19 (September 20, 2022): 9422. http://dx.doi.org/10.3390/app12199422.
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Forecasting, commonly used in econometrics, meteorology, or energy consumption prediction, is the field of study that deals with time series data to predict future trends. Former studies have revealed that both traditional statistical models and recent deep learning-based approaches have achieved good performance in forecasting. In particular, temporal convolutional networks (TCNs) have proved their effectiveness in several time series benchmarks. However, presented TCN models are too heavy to deploy on resource-constrained systems, such as edge devices. As a resolution, this study proposes a stride–dilation mechanism for TCN that favors a lightweight model yet still achieves on-pair accuracy with the heavy counterparts. We also present the Chonnam National University (CNU) Electric Power Consumption dataset, the dataset of energy consumption measured at CNU by smart meters every hour. The experimental results indicate that our best model reduces the mean squared error by 32.7%, whereas the model size is only 1.6% compared to the baseline TCN.
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Gajdzik, Bożena, Włodzimierz Sroka, and Jolita Vveinhardt. "Energy Intensity of Steel Manufactured Utilising EAF Technology as a Function of Investments Made: The Case of the Steel Industry in Poland." Energies 14, no. 16 (August 20, 2021): 5152. http://dx.doi.org/10.3390/en14165152.
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The production of steel in the world is dominated by two types of technologies: BF + BOF (the blast furnace and basic oxygen furnace, also known as integrated steel plants) and EAF (the electric arc furnace). The BF + BOF process uses a lot of natural resources (iron ore is a feedstock for steel production) and fossil fuels. As a result, these steel mills have a significantly negative impact on the environment. In turn, EAF technology is characterised by very low direct emissions and very high indirect emissions. The raw material for steel production is steel scrap, the processing of which is highly energy-consuming. This paper analyses the energy intensity of steel production in Poland as a function of investments made in the steel industry in the years 2000–2019. Statistical data on steel production in the EAF process in Poland (which represents an approximately 50% share of the steel produced, as the rest is produced utilising the BF + BOF process) was used. Slight fluctuations are caused by the periodic switching of technology for economic or technical reasons. The hypothesis stating that there is a relationship between the volume of steel production utilising the EAF process and the energy consumption of the process, which is influenced by investments, was formulated. Econometric modelling was used as the research method and three models were constructed: (1) a two-factor power model; (2) a linear two-factor model; and (3) a linear one-factor model. Our findings show that the correlation is negative, that is, along with the increase in technological investments in electric steel plants in Poland, a decrease in the energy consumption of steel produced in electric furnaces was noted during the analysed period.
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Thakare, Sameer, Neeraj Dhanraj Bokde, and Andrés E. Feijóo-Lorenzo. "Forecasting different dimensions of liquidity in the intraday electricity markets: A review." AIMS Energy 11, no. 5 (2023): 918–59. http://dx.doi.org/10.3934/energy.2023044.
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<abstract><p>Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models.</p></abstract>
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Sun, Zhenhua, Lingjun Du, and Houyin Long. "Regional Heterogeneity Analysis of Residential Electricity Consumption in Chinese Cities: Based on Spatial Measurement Models." Energies 16, no. 23 (November 30, 2023): 7859. http://dx.doi.org/10.3390/en16237859.
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The share of electricity consumption by urban and rural residents in China’s overall electricity consumption is very close to that of the tertiary sector, which has become an important driver of China’s electricity consumption growth. At the same time, due to the mismatch between China’s regional resource endowments and the level of regional development, the regional supply and demand situation for electricity varies. Therefore, it is urgent to clarify the regional differences in residential electricity consumption and the factors affecting it, and accordingly adopt targeted and feasible measures to regulate residential electricity consumption. This article includes data from 285 Chinese prefecture-level cities from 2006 to 2019, and adopts a “three lines” method of region-partitioning (Qinling–Huaihe line, Huhuanyong line, and Shanhaiguan line) to divide four regions. We used spatial econometric models to examine residential electricity consumption and its influencing factors in China from the standpoint of regional heterogeneity. The results show that there is significant regional heterogeneity in residential electricity consumption in China, and the difference between the north of the Shanhaiguan line and other areas is significant. Moreover, there is a positive spatial correlation in the per capita domestic electricity consumption of urban residents, and each influencing factor has obvious regional heterogeneity, among which household appliances are the significant influencing factor. Based on the regional heterogeneity of residential electricity consumption, management measures should be formulated according to local conditions, and the supply of electricity should be ensured by strengthening multidimensional initiatives.
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Peng, Fei, Ye Zhang, Guohua Song, Jianchang Huang, Zhiqiang Zhai, and Lei Yu. "Evaluation of Real-World Fuel Consumption of Hybrid-Electric Passenger Car Based on Speed-Specific Vehicle Power Distributions." Journal of Advanced Transportation 2023 (February 27, 2023): 1–13. http://dx.doi.org/10.1155/2023/9016510.
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Fuel consumption differs between the hybrid electric vehicle (HEV) and the conventional vehicle (CV). However, traditional fuel consumption models developed for CVs are commonly applied to HEVs, which leads to uncertainties in the quantitative evaluation of energy consumption for passenger cars in traffic road networks. Considering the internal combustion engine (ICE) operating modes of hybrid vehicles among varying vehicle specific power (VSP) demand, we present a method to incorporate the HEV ICE speed to develop speed-specific VSP distributions for real-world driving conditions. Using vehicle trajectory and fuel consumption data in real traffic conditions, the results of this study show that the application of methods developed for CVs leads to a significant underestimation of fuel consumption for HEVs when the average speed is in the high-speed range (over 50 km/h) and a significant overestimation of fuel consumption when the average speed is in the low-speed range (below 30 km/h). The average relative error of the measured fuel consumption factor in each speed bin is 7.1% compared with real-world observations, which is an unacceptably large error. This paper proposes a method to develop the speed-specific VSP distribution, considering whether the internal combustion engine (ICE) of HEVs is on or off. This approach reduces the average relative error of the obtained fuel consumption compared with real-world observations to 2.2%, and the measuring accuracy at different average speeds is significantly improved. This method enhances the functionality and applicability of the VSP theory-based traffic energy model for HEVs.
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Myszczyszyn, Janusz, and Błażej Suproń. "Relationship among Economic Growth, Energy Consumption, CO2 Emission, and Urbanization: An Econometric Perspective Analysis." Energies 15, no. 24 (December 19, 2022): 9647. http://dx.doi.org/10.3390/en15249647.
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The key goal of this research was to figure out the short and long run relationship between environmental degradation caused by carbon dioxide (CO2) emissions and energy consumption, the level of GDP economic growth, and urbanization in the Visegrad Region countries (V4). The study used data from the years 1996–2020. In the methodological area, ARDL bound test, and ARDL and ECM models were used to determine the directions and strength of interdependence. The results show that in the case of some V4 countries (Poland, Slovakia, and Hungary), changes in the urbanization rate affect CO2 emissions. Moreover, it was confirmed that the phenomenon of urbanization influences the enhanced energy consumption in the studied countries. In the case of individual countries, these relationships were varied, both unidirectional and bidirectional. Their nature was also varied—there were both long and short-term relationships. These findings suggest that the V4 countries should increase renewable and ecological energy sources. It is also recommended to enhancement energy savings in the areas of both individual and industrial consumption by promoting low-emission solutions. This should be done while considering changes in urbanization.
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Sinchuk, Oleg M., Ryszard Strzelecki, Igor O. Sinchuk, Andriy I. Kupin, Tatiana M. Beridze, and Кyrylo V. Budnikov. "Informational aspects at model of power consumption by main drainage facilities of iron-ore mining enterprises." Herald of Advanced Information Technology 4, no. 4 (December 23, 2021): 341–53. http://dx.doi.org/10.15276/hait.04.2021.5.
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The work investigates into variable informational approaches to modeling power consumption by main drainage facilities of ore mining enterprises with underground mining method. Methodological recommendations for using the models are also designed. The research deals with general methodological approaches to model formation with both power consumption indices for drainage facilities and corresponding costs. Logistics of model formation is substantiated, namely, combination of classic multifactor regression modeling with modern digital modeling methods – automated control systems used for drainage facilities. Principles of building fuzzy logic controllers and algorithms of their functioning under multichannel control are determined in detail. The improved fuzzy logic-based variant is proposed and combined, with correlation analysis, to provide the basis for developing algorithms of the automated control systems of electric power consumption. There is an example of developing a “road map” for implementing a generalized algorithm for automated control systems power flows for two current cases – a selective tariff with limited daily contract-based power consumption and that with a variable tariff. It is established that application of the two-rate hourly tariff with its conditional distribution (Night/Peak) instead of the three-rate tariff (Night/Half-Peak/Peak) on a single-use basis leads to a thirteen percent increase of daily power costs with a single-channel control of the ore flow and a seven percent increase with two-channel control (ore flow and drainage simultaneously). The use of fuzzy logic controllers enables minimizing these losses.
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Szaruga, Elżbieta, and Elżbieta Załoga. "Qualitative–Quantitative Warning Modeling of Energy Consumption Processes in Inland Waterway Freight Transport on River Sections for Environmental Management." Energies 15, no. 13 (June 25, 2022): 4660. http://dx.doi.org/10.3390/en15134660.
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The article concerns the assessment of the energy consumption of inland waterway freight transport on river sections in the context of environmental management. The research question was: Does the choice of the route determine the total energy consumption of inland waterway transport and therefore affect the potential of cargo transport of this mode? The article aims to indicate the directions of energy consumption by inland waterway freight transport depending on the route selection, the volume of transport, and the length of the route. The study was carried out on nine sections of the Odra River in Poland during the years 2015–2020. Statistical and econometric techniques were used, i.e., ANOVA, generalized linear models, Eta coefficients, Lasso and Ridge regularization, and X-average control charts (Six Sigma tool). Based on early warning models, river sections were identified that favor the rationalization of energy consumption in terms of the network. The sensitivity of the energy consumption of inland waterway transport to changes in the average distance and in the volume of transport was examined. With the use of Six Sigma tools, the instability of the energy consumption processes of inland waterway transport was identified, paying attention to the source of the mismatch, which was the increase in the average transport distance in the sections, where energy consumption increased due to the operational and navigation conditions of these sections.
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Holland, Stephen P., Erin T. Mansur, Nicholas Z. Muller, and Andrew J. Yates. "Decompositions and Policy Consequences of an Extraordinary Decline in Air Pollution from Electricity Generation." American Economic Journal: Economic Policy 12, no. 4 (November 1, 2020): 244–74. http://dx.doi.org/10.1257/pol.20190390.
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Using integrated assessment models, we calculate the economic value of the extraordinary decline in emissions from US power plants. Annual local and global air pollution damages fell from $245 to $133 billion over 2010–2017. Decomposition shows changes in emission rates and generation shares among coal and gas plants account for more of this decline than changes in renewable generation, electricity consumption, and damage valuations. Econometrically estimated marginal damages declined in the East from 8.6 to 6 cents per kWh. Marginal damages increased slightly in the West and Texas. These estimates indicate electric vehicles are now cleaner on average than gasoline vehicles. (JEL H23, L94, Q53, Q58)
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Zhou, Xing, Quan Guo, and Ming Zhang. "Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective." Energies 14, no. 21 (November 4, 2021): 7343. http://dx.doi.org/10.3390/en14217343.
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Under the Belt and Road concepts of mutual benefit and win–win cooperation, China is strengthening its energy cooperation with other countries. We used several econometric models and social network analysis models to study the impacts of China’s outward foreign direct investment (OFDI) on the host and home countries. We first examined China’s OFDI location preference and analysed the effects of OFDI on energy consumption in host countries. Meanwhile, we observed the impact of the reverse spillover effect of OFDI on China’s energy efficiency. The results indicate that (1) the impact of China’s OFDI on energy consumption in host countries has been lower than that on neighbouring countries, and increased significantly after 2014. (2) The space network of energy consumption in Belt and Road countries has a strict hierarchical structure. However, it was disbanded by the Belt and Road policy in 2014. The network centres are situated primarily in Middle Eastern and European countries, and the network’s periphery is mainly in South-East and West Asian countries. (3) The reverse spillover effects of OFDI, FDI, domestic R&D absorptive capacity, human capital, and financial development levels are conducive to improving China’s energy efficiency whereas regional professionalism does the opposite.
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Majewska, Agnieszka, and Urszula Gierałtowska. "Impact of Economic Affluence on CO2 Emissions in CEE Countries." Energies 15, no. 1 (January 4, 2022): 322. http://dx.doi.org/10.3390/en15010322.
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There is huge evidence for a relationship between economic growth and environmental degradation. One of the causes of environmental degradation is CO2 emission which is added to the atmosphere through human activities and excessive industrialization. The aim of this research is to examine the relationship between CO2 emissions and measures of wealth in countries of Central and Eastern Europe between 2000 and 2019. The paper extends the research on economic affluence by taking into consideration two measures of economic growth, in addition to GDP, the HDI index is included. The basis for the investigation is the EKC concept. All analyses are based on econometric models with GDP and the HDI index as independent variables. The results are not conclusive and there is no one model which best describes the relationship between CO2 emissions and economic growth. Verification of the models indicates the better fit of models with the HDI index as the measure of affluence. Moreover, the study confirms that the key factors affecting CO2 emissions are energy consumption per capita which leads to an increase in CO2 emissions, and renewable energy consumption which reduces CO2 emissions. Therefore, technological changes and an increase in human awareness of global sustainability are required.
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Chen, Chenxi, Yang Song, Xianbiao Hu, and Ivan G. Guardiola. "Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach." Journal of Advanced Transportation 2020 (November 22, 2020): 1–12. http://dx.doi.org/10.1155/2020/8850654.
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This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.
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Gajdzik, Bożena, and Włodzimierz Sroka. "Resource Intensity vs. Investment in Production Installations—The Case of the Steel Industry in Poland." Energies 14, no. 2 (January 15, 2021): 443. http://dx.doi.org/10.3390/en14020443.
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Resource intensity is a measure of the resources needed for the production, processing and disposal of good or services. Its level decides on the costs the companies have to bear both for production and for environmental protection, which in turn have a crucial importance for their competitiveness. Given these facts, our study analyses the issues of resource intensity in the Polish steel industry in correlation to investments made, and more specifically, to the impact of investments on the consumption of energy media used during steel production. Its key element is the development of econometric models presenting the impact of investments on resource consumption in steel production in Poland. Electricity and coke consumption were analysed according to manufacturing installation. The research was carried out on the basis of statistical data for the period of 2004–2018. The obtained findings confirmed the impact of the increase in investment on the decrease in the resource intensity in steel production in Poland. These facts have implications for both policy makers, as they confirm the thesis on a direct correlation between investments in technology and a reduction in resource intensity (environmental protection), as well as company managers. In the case of the latter, the data show the actions which companies should focus on in their activities.
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Molocchi, Andrea. "Electric cars or high-efficiency transport networks?" ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, no. 1 (July 2010): 13–29. http://dx.doi.org/10.3280/efe2010-001002.
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The main drivers of the electric cars diffusion which is projected in the next decade are to be seen in the industry effort to create new spaces in mature car markets (supply side), and in demand side effects of current climate mitigation policies in the transport sector, focused on CO2 emissions and energy efficiency of new vehicles sold. Indeed the energy performances of electric vehicles are projected to be highly variable; the final results of fleet average comparisons with internal combustion engines vehicles will be affected by at least: a) the real energy efficiency of the EV models (in particu- lar by their weight), b) the battery efficiency rate; c) the average energy losses of the national grids, and d) the national thermoelectric generation efficiency. For example, if sensitivity analysis on Germany or USA power plants efficiencies is undergone, EV primary energy consumptions result to be respectively +8% and +30% higher than conventional ICE cars. Moreover, EV credentials in terms of transport external costs reduction are very poor, particularly for congestion. If we look at research results comparing the external costs of different transport modes, high net benefits may be alternatively seen in public transport and rail based mobility (and also in short sea shipping for certain freight transport types) either in terms of energy efficiency and external costs reduction. An EU27 wide transport indicator based analysis is provided in chapter 4 to better highlight this "structural" additional driver of transport external costs, which has strictly little to do with vehicle level efficiency, rather it represents an efficiency in urban planning and infrastructural planning: a "system" efficiency in providing availability and access to highly energy efficient transport modes and services. In the final chapter recommendations for transport and energy European policies are provided, starting from a target setting based on external costs indicators (capturing also and not exclusively the energy efficiency and savings potential offered by transport infrastructures and vehicles), followed by an urgently needed Long term Action plan for railways networks and intermodality development. As to EVs, it is recommended to regulate them under an extension of the current EU CO2/km average target approach, by setting a common (final) energy consumption efficiency standard for all car innovations. Public funds collected from external costs road charging may be better concentrated by EU and Member States on this infrastructural Action Plan rather than on urban electricity grids for EVs.
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Halkos, George, and Eleni-Christina Gkampoura. "Assessing Fossil Fuels and Renewables’ Impact on Energy Poverty Conditions in Europe." Energies 16, no. 1 (January 3, 2023): 560. http://dx.doi.org/10.3390/en16010560.
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The disadvantages of fossil fuels and their impact on the environment have made the transition to renewable energy sources essential to cover our energy needs. However, different energy resources have a different impact on energy poverty conditions in the world, an issue that is important to examine and properly address. This study examines the impact that fossil fuels final energy consumption in households per capita and renewables and biofuels final energy consumption in households per capita have on energy poverty conditions in Europe, using panel data from 28 European countries for the time period 2004–2019 and static and dynamic regression models, while also performing various econometric tests. The findings indicate that GDP per capita and fossil fuels are linked to an inverse relationship to energy poverty conditions. Renewables and biofuels are also linked to an inverse relationship to the inability to keep homes adequately warm and the presence of leaks, damp, or rot in the dwelling, but they could be considered a driver of arrears on utility bills. In addition, a comparative analysis between Sweden, Germany, and Greece and their conditions on energy poverty and energy transition was conducted, highlighting the differences existing between the three European countries. The findings of the research can be useful for governments and policy makers to develop strategies that promote energy transition while protecting energy consumers.
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Dehdar, Fatemeh, Nuno Silva, José Alberto Fuinhas, Matheus Koengkan, and Nazia Nazeer. "The Impact of Technology and Government Policies on OECD Carbon Dioxide Emissions." Energies 15, no. 22 (November 14, 2022): 8486. http://dx.doi.org/10.3390/en15228486.
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This study investigated the effect of technology and government policies on carbon dioxide (CO2) emissions in 36 Organisation for Economic Co-operation and Development (OECD) countries from 1994 to 2015. This empirical investigation uses econometric models, such as panel quantile regression and ordinary least squares (OLS). The research uses the method proposed by Lin and Ng in 2015 to deal with parameter heterogeneity across countries that identified two separate groups. The empirical results indicated that Gross Domestic Product (GDP), fossil fuel consumption, industrialisation and taxation to GDP intensify CO2 emissions. In contrast, urbanisation (% of the total population), environmental patents, and environmental tax as a percentage of total tax reduce CO2 gas emissions. Estimates with homogeneity preserve the signs of the parameters but reveal substantial differences in intensity and that environmental tax revenues (as % of GDP and % of tax) are only statistically significant for our studied group 1. The conclusions of this study have important policy implications. The effect of industrialisation on environmental degradation is an observable fact. When the country reaches the allowable thresholds, it needs to maximize energy consumption. Policymakers should design policies that help them to promote environmentally sustainable economic growth by imposing and accumulating environmental taxes. In addition, environmental taxes, the discharge system and credit could support the modification of in-industrial structures and modes of economic growth. Policymakers should also use policies that encourage trade in nuclear-generated electricity to neighbouring OECD countries.
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Zhao, De, Hua Wang, and Zhiyuan Liu. "Charging-Related State Prediction for Electric Vehicles Using the Deep Learning Model." Journal of Advanced Transportation 2022 (August 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/4372168.
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Electric vehicles (EVs) are becoming the potential contender for the conventional gasoline vehicles in view of the environment-friendly and energy-efficient characteristics. The prediction of EV charging-related states (defined in this study as home charge, outside charge, home stop, outside stop, low-battery travel, and high-battery travel) could help to identify the future charging demand (power consumption) of EV individuals. Specifically, it could guide the operation and management of charging facilities and also provide tailored charger availability information based on users’ real-time locations. This study aims to predict charging-related states of individual EVs using a deep learning approach. We first propose a tangible approach to convert EV trajectory data into state sequences and then develop a bidirectional gated recurrent unit model with attention mechanism (Bi-GRU-Attention) to forecast EV states. A sensitivity analysis is conducted to tune and/or calibrate parameters in the model based on plug-in hybrid EV trajectories dataset collected in Shanghai, China. Experiment results show that (i) the proposed method could achieve an average accuracy of 77.15% with a 1-hour prediction length and it outperforms the baseline models for all tested prediction lengths; (ii) it is also revealed that the prediction accuracy varies dramatically with different states and time periods. Among all states, the proposed model has a higher prediction accuracy on “home stop” (89.0%). As for time periods, the EV states around 08:00 am and 04:00 pm are hard to predict, and a comparatively low prediction accuracy (close to 60%) is obtained; and (iii) the stability and robustness analysis implies that the proposed model is stable and insensitive to SOC noise or season.
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Costantini, Valeria, Mariagrazia D’Angeli, Martina Mancini, Chiara Martini, and Elena Paglialunga. "An Econometric Analysis of the Energy-Saving Performance of the Italian Plastic Manufacturing Sector." Energies 17, no. 4 (February 8, 2024): 811. http://dx.doi.org/10.3390/en17040811.
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In a scenario characterised by mitigation concerns and calls for greater resilience in the energy sector, energy audits (EAs) emerge as an essential mean for enhancing end-use energy consumption awareness and efficiency. Such a tool allows us to assess the different energy carriers consumed in a productive sector, offering insight into existing energy efficiency improvement opportunities. This opens avenues for research to devise an econometrics-based methodology that encapsulate production sites and their environmental essentials. This paper contributes to the literature by exploiting the EAs received by the Italian National agency for New technologies, Energy, and Sustainable Economic Development (ENEA) in 2019 from the Italian plastics manufacturing sector, matched with Italian firm-based data extracted from the Analisi Informatizzata delle Aziende Italiane (Italian company information and business intelligence) (AIDA) database. In particular, we investigate how the implementation of energy efficiency measures (EEMs) is influenced by a set of contextual factors, as well as features relating to the companies and EEMs themselves. The empirical investigation focuses on the EAs submitted to ENEA in 2019, which was strategically chosen due to its unique data availability and adequacy for extensive analysis. The selection of 2019 is justified as it constitutes the second mandatory reporting period for energy audits, in contrast to the 2022 data, which are currently undergoing detailed refinement. In line with the literature, the adopted empirical approach involves the use of both the OLS and logistic regression models. Empirical results confirm the relevance of economic and financial factors in guiding the decisions surrounding the sector’s energy performance, alongside the analogous influence of the technical characteristics of the measures themselves and of the firms’ strategies. In particular, the OLS model with no fixed effects shows that a one-percent variation in investments is associated with an increase in savings performance equal to 0.63%. As for the OLS model, including fixed effects, the elasticity among the two variables concerned reaches 0.87%, while in the logistic regression, if the investment carried out by the production sites increases, the expected percentage change in the probability that the energy-saving performance is above its average is about 187.77%. Contextual factors that prove to be equally influential include the incentive mechanism considered and the traits of the geographical area in which the companies are located. Relevant policy implications derived from this analysis include the importance of reducing informational barriers about EEMs and increasing technical assistance, which can be crucial for identifying and implementing effective energy solutions.
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Brazovskaia, Viktoriia, Svetlana Gutman, and Andrey Zaytsev. "Potential Impact of Renewable Energy on the Sustainable Development of Russian Arctic Territories." Energies 14, no. 12 (June 21, 2021): 3691. http://dx.doi.org/10.3390/en14123691.
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In recent decades, there has been a positive trend in world politics in the field of promoting territories’ sustainable development. At the same time, one of the most relevant areas is to promote the transition to renewable energy sources (RES), which correspond to one of the UN’s goals—Sustainable Development Goal 7 (SDG 7) “Ensuring universal access to affordable, reliable, sustainable and modern energy sources for all”. This article is devoted to the study of the renewable energy sources’ impact on the sustainable development of the Russian Arctic zone. The authors chose the level of carbon dioxide (CO2) emissions as an indicator reflecting the impact of RES on sustainable development, since this factor is one of the main factors for assessing trends in the activities of countries aimed at achieving progress on most of the Sustainable Development Goals of territories. The hypothesis of the relationship between the use of renewable energy sources and the achievement of progress on the Sustainable Development Goals, one of the indicators of which is the level of CO2 emissions, was tested and confirmed. An econometric analysis of panel data for 15 countries that are actively implementing the concept of sustainable development, including decarbonizing policies, was carried out, where the resulting indicator for achieving progress on the SDG was the amount of CO2 emissions. The factors influencing the resulting variable were indicators selected based on a review of existing models, as well as indicators of the Sustainable Development Goals’ achievement. Using an econometric analysis of interdependence, the indicators of progress towards the Sustainable Development Goals that are more likely to have an impact on the level of CO2 emissions were identified. These are electricity consumption, the share of renewable energy sources in the energy balance, the average per capita income of the population, and carbon intensity. Based on the results obtained, it can be concluded that renewable energy sources are a factor contributing to the achievement of progress on the Sustainable Development Goals. The results obtained are also applicable to the Arctic region, since all countries that have territories in the Arctic zone adhere to the policy of decarbonization and try to reduce the use of fossil fuels.
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Karpenko, Sergey, and Nadezhda Karpenko. "Analysis and modeling of regional electric power consumption subject to influence of external factors." Energy Safety and Energy Economy 3 (June 2021): 12–17. http://dx.doi.org/10.18635/2071-2219-2021-3-12-17.
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Electric power consumption along with a large variety of factors affecting it can be forecasted and modelled by using econometric forecasting methods, including time series and correlation and regression analysis. For the purpose of this research, electric power consumption in the Moscow Region, Russia, was modelled with consideration of economic and climate factors based on 2019–2020 power usage data. A multiplicative model for regional electric power consumption and correlations between electric power consumption and an air temperature as well as a total number of cloudy days a month were built. The results will be helpful for analyzing and forecasting of processes involved in power consumption.
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Rakhmonov, I., N. Niyozov, and K. Li. "DEVELOPMENT OF CORRELATION AND REGRESSION MODELS OF ELECTRIC ENERGY INDICATORS OF THE EQUIPMENT WITH CONTINUOUS NATURE OF PRODUCTION." Technical science and innovation 2019, no. 4 (December 12, 2019): 203–8. http://dx.doi.org/10.51346/tstu-01.19.4.-77-0039.
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The article presents an analysis of the use of correlation-regression analysis, which is based on the methods of mathematical statistics and probability theory in the study of the power consumption of enterprises with equipment of continuous production. On the basis of the annual power consumption schedule of the electric steel-smelting shop in a monthly time section, mathematical models have been developed for the power consumption parameters. And also, on the basis of statistical data with the use of a mathematical method, mathematical expressions were obtained for the electric power consumption and the specific consumption for the main equipment of the electric steel-smelting shop. In order to assess the adequacy of the developed mathematical models, mathematical models of the total and specific consumption of their power consumption are compared with actual data. The comparison results show high reliability of the power consumption modes of the main equipment of the facility in question. The analysis of the values of forecast errors with low error rates determines the adequacy of the developed mathematical models of the parameters of power consumption in terms of power consumption and specific consumption for the main equipment of the electric steel-smelting shop. In this regard, they can be used to determine the predicted values of the parameters of power consumption in electric steelmaking equipment.
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Ozawa, Kazuhiro, ’Takahide Niimura, and Tomoaki Nakashima. "Fuzzy Time-Series Model of Electric Power Consumption." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 3 (May 20, 2000): 188–94. http://dx.doi.org/10.20965/jaciii.2000.p0188.
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In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performance of the models are evaluated by vagueness and α-cuts. The proposed fuzzy Auoregression model represents the rich information of uncertainty that the original data contain, and it can be a powerful tool for flexible decision-making with uncertainty. The fuzzy AR model can also be constructed in relatively simple procedure compared with the conventional approaches.
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Borovsky, Andrey, and Andrey Yumenchuk. "Stochastic Models of Electricity Consumption." System Analysis & Mathematical Modeling 6, no. 1 (March 30, 2024): 31–46. http://dx.doi.org/10.17150/2713-1734.2024.6(1).31-46.
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The article considers a stochastic model of electricity consumption based on the convolution theory. It is assumed that the processes of switching on and off energy consumption on a city scale depend on a large number of independent random factors and have the form of a normal probability distribution. The functions of switching on and off the load are presented, graphs are plotted. The comparison of the intelligent power supply system with the existing power supply system in the Russian Federation is carried out. The reasons for the slow introduction of smart grids in the Russian Federation are revealed. At the moment, smart grids are not very popular in the Russian Federation, while in the countries of the European Union, the USA and China, projects related to the development, production of components and the widespread introduction of smart grids are actively receiving state financial support. Thus, the European Union allocates more than a billion dollars annually for projects in the field of electric power and smart grids. These funds are allocated according to the European Green Deal strategy. At the same time, the State Grid Corporation of China has introduced the concept of the "Global Energy Internet", which, in accordance with the government's expectation, will stimulate the smart grid market in the country. As in other countries of the world, the US electric power industry is also facing problems related to rising utility prices, fluctuations in peak load and the need to reduce carbon dioxide emissions. The consequence of this was the adoption at the end of 2021 of an "infrastructure" law providing for large-scale investments in green energy projects.
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Wiśniewski, Jerzy Witold. "An econometric model of household electricity consumption. A case study of Poland." Scientific Papers of Silesian University of Technology. Organization and Management Series 2023, no. 178 (2023): 705–16. http://dx.doi.org/10.29119/1641-3466.2023.178.39.
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Purpose: Electrical energy is in this day and age fundamental to the functioning of every household. The use of gas, heating oil or solid fuels for home heating also involves the supply of electricity. Today, electric power is the most environmentally friendly source of energy. Experience has shown that substitution of an oil heating system with a heat pump resulted in significant financial savings for the household. Design/methodology/approach: The purpose of this paper shall entail the construction of a multi-equation econometric model describing the mechanisms of electricity consumption in a specified household. The model takes the nature of a system of interdependent equations. Described shall be the monthly volume of electricity consumption, payment amount of for this energy (in PLN), and price per 1 MWh in a given month. Monthly time series from September 2015 to November 2022 have been used, which resulted in a time series with a count of 87 statistical observations. Findings: The econometric model of household electricity consumption presented in this paper confirms both the hypothesis about feedback between the variables USAGE and PRICE as well as the recursive effect of electricity consumption volume on its value in monetary units. In addition to the cognitive value of the econometric modeling results obtained, the empirical tool constructed makes enables forecast estimation of the energy consumption volume, its value and unit price in subsequent months, for at least 12 consecutive months. Practical implications: This type of research has great practical utility. They make it easier for rational interaction between electricity sellers and specific consumers. Social implications: Knowledge about the mechanisms of electricity consumption on the farm home may influence the rationalization of consumption and spending. This type of rationalization should have a positive impact on the environment natural, contributing to the reduction of greenhouse gas emissions. Originality/value: The novelty and originality of the work are the identification of the mechanism behavior of a single household consuming energy electric. Feedback between magnitudes revealed electricity consumption and its price on a specific farm home. The recipients of the work will be electricity sellers, consumers, and researchers of household market behavior. Keywords: econometric model, electricity consumption, interdependent equations. Category of the paper: Research paper. Case study.
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Kazemzadeh, Emad, Matheus Koengkan, and José Alberto Fuinhas. "Effect of Battery-Electric and Plug-In Hybrid Electric Vehicles on PM2.5 Emissions in 29 European Countries." Sustainability 14, no. 4 (February 15, 2022): 2188. http://dx.doi.org/10.3390/su14042188.
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The contribution of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) to mitigating/reducing fine particulate matter (PM2.5) emissions was researched through a panel of 29 European countries from 2010 to 2019, using the econometric technique of method of moments quantile regression (MM-QR). This research is innovative by connecting the increasing use of electric vehicles with PM2.5 emissions and using the MM-QR to explore this relationship. Two models were estimated to analyse their contribution to reducing PM2.5 in European countries. The nonlinearity of the models were confirmed. The statistical significance of the variables is strong for the upper quantiles (75th and 90th), resulting from the effectiveness of European policies to improve the environment. Electric vehicles (BEVs and PHEVs), economic growth, and urbanisation reduce the PM2.5 problem, but energy intensity and fossil fuel consumption aggravate it. This research sheds light on how policymakers and governments can design proposals to encourage electric vehicle use in European countries. To achieve the long-term climate neutral strategy by 2050, it is imperative to implement effective policies to reduce the consumption of fossil fuels and promote the adoption of electric vehicles using renewable energy sources.
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Benavides, Carlos, Sebastián Gwinner, Andrés Ulloa, José Barrales-Ruiz, Vicente Sepúlveda, and Manuel Díaz. "Bus Basis Model Applied to the Chilean Power System: A Detailed Look at Chilean Electric Demand." Energies 17, no. 14 (July 13, 2024): 3448. http://dx.doi.org/10.3390/en17143448.
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This paper presents a methodology to forecast electrical demand for the Chilean Electrical Power System considering a national, regional, district and bus spatial disaggregation. The methodology developed was based on different kinds of econometric models and end-use models to represent the massification of low carbon emission technologies such as electromobility, electric heating, electric water heating, and distributed generation. In addition, the methodology developed allows for the projection of the electric demand considering different kinds of clients as regulated and non-regulated clients, and different economic sectors. The model was applied to forecast the long-term electricity demand in Chile for the period 2022–2042 for 207 districts and 474 buses. The results include projections under the base case and low carbon scenarios, highlighting the significant influence of new technologies on future demand.
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Gu, Huaying, and Chaoqun Han. "Analysis of China’s Pure Electric Vehicle Sales Based on Spatial Econometric Models." International Journal of Economics and Finance 13, no. 1 (December 5, 2020): 12. http://dx.doi.org/10.5539/ijef.v13n1p12.
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This paper empirically investigates the spatial dependence and serial correlation structures among different China’s brands of pure electric vehicle (EV) sales using spatial econometric models. Based on the newly proposed economic distance spatial weight matrix, the empirical results show that EV endurance mileage, power battery capacity, charging time, government subsidy, retail price, and each brand market share have important impacts on EV sales. The per capita disposable income of urban households, gasoline price, loan rate and the number of charging pile are statistically significant determinants of EV sales. In particular, the improvements of the number of charging pile and the rise of gasoline price can increase EV sales, while the rise of loan rate or tight monetary policy may increase the consumers’ cost of purchasing EVs and then decrease EV sales. Another interesting finding is that though the per capita disposable income of urban households increases the EV sales decreases. A plausible explanation would seem to be that the impact of the per capita disposable income of urban households on the EV sales is offset by the decline in government subsidies or the incomplete infrastructures such as the inconvenient of charging stations. Besides, the significantly positive spatial dependence and serial correlation exist among EV manufactures indicates that when developing EV sales strategies, EV manufacturers must consider not only the properties of the EVs they produce, but also the properties of similar types of EVs produced by other brands in the EV market.
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Peña-Guzmán, Carlos, and Juliana Rey. "Forecasting residential electric power consumption for Bogotá Colombia using regression models." Energy Reports 6 (February 2020): 561–66. http://dx.doi.org/10.1016/j.egyr.2019.09.026.
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Tolegenova, G., A. Zakirova, and A. Astankevich. "Models and methods for forecasting electrical loads." BULLETIN of L.N. Gumilyov Eurasian National University. Technical Science and Technology Series 143, no. 2 (June 30, 2023): 260–68. http://dx.doi.org/10.32523/2616-7263-2023-143-2-260-268.
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Currently, the prediction of electrical loads is an important task. On the basis of forecasts, the operating modes of stations, the network configuration are calculated, the efficiency and quality of electric power is estimated, the schedule of repair work is calculated, etc. The electric load forecasting model is one of the foresight tools for making management decisions when managing electric power systems. This article consists in the construction, evaluation and comparative study of various models for forecasting electricity consumption. The following approaches and methods in forecasting were studied and analyzed: neural network, neuro fuzzy.
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Afanasiev, А. Y., N. A. Rybushkin, and K. A. Kilimanov. "MODELING AND OPTIMIZATION ON ENERGY CONSUMPTION OF A HYBRID POWER INSTALLATION FOR A VEHICLE." Proceedings of the higher educational institutions. ENERGY SECTOR PROBLEMS 20, no. 11-12 (February 27, 2019): 133–43. http://dx.doi.org/10.30724/1998-9903-2018-20-11-12-133-143.
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The object of research is a hybrid power installation of a bus with a diesel engine and a synchronous electric motor. The purpose of the work is the development of engineering models of a synchronous electric motor and a diesel engine, the search for optimal electric currents control laws for electric motor and optimal bus moving laws with a hybrid power installation for general energy consumption. Synchronous electric drive and internal combustion engine models were used, convenient for research, analytical and numerical methods of parametric optimization and optimal control were used. Parameter values and laws of vehicle motion with energy saving are obtained.
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Kechkin, Aleksei Olegovich, Aleksandr Sergeevich Plekhov, and Oleg Stanislavovich Khvatov. "Improving efficiency of dredger electric power system." Vestnik of Astrakhan State Technical University. Series: Marine engineering and technologies 2022, no. 1 (February 28, 2022): 50–57. http://dx.doi.org/10.24143/2073-1574-2022-1-50-57.
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Most river dredgers are equipped with several electric positioning drives, a soil pump electric drive and a hydraulic washing pump. The listed electric drives are the main consumers of electricity from the dredger diesel generators. It is necessary to change the structure of the ship electric power network in order to reduce fuel consumption by diesel generators. The energy characteristics of the dredger electric drive system with a different configuration of the converter equipment are determined. Simulation models of the dredger DC and AC power systems have been developed. The models were built using MATLAB Simulink software and SimPowerSystem library. Application of simulation models made it possible to assess the efficiency of using a DC bus in the power system of the dredger. The circuits of the dredger electric power system, electric drives of cross-dredging winches are illustrated. The operation modes of electric drives of cross-dredging winches are analyzed. The necessity of analyzing the operation of electric drives and of developing the control signals for the braking resistors of the frequency converter in the power control system is revealed. The parameters of the presented electric drives and their values are considered. The expressions for calculating the total power of the system nodes are given, taking into account the characteristics of the operating modes of the dredger electric drives based on an alternating current network. Using the developed version of the power system contributes to saving capital costs and specific fuel consumption. Modernization of the power system helps reduce the power of diesel generators, increase the efficiency of the system and reduce the power of the ventilation system of compartments with winch frequency converters, due to regenerative braking by the electric drives.
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Song, Xinfu, Gang Liang, Changzu Li, and Weiwei Chen. "Electricity Consumption Prediction for Xinjiang Electric Energy Replacement." Mathematical Problems in Engineering 2019 (March 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/3262591.
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In recent years, the phenomenon of wind and solar energy abandoned in Xinjiang’s new energy has become severe, the contradiction between the supply and demand of the power grid is obvious, and the proportion of power in the energy consumption structure is relatively low, thus hindering the development of Xinjiang’s green power. In this context, the focus of Xinjiang’s power has shifted to promote the development of electric energy replacement. Therefore, using the Xinjiang region as an example, we first select the important indicators such as the terminal energy substitution in Xinjiang, added value of the secondary industry, population, terminal power consumption intensity, and per capita disposable income. Subsequently, eight combined forecasting models based on the grey model (GM), multiple linear regression (MLR), and error back propagation neural network (BP) are constructed to predict and analyse the electricity consumption of the whole society in Xinjiang. The results indicate the optimal weighted combination forecasting model, GM-MLR-BP of the induced ordered weighted harmonic averaging operator (IOWHA operator), exhibits better prediction accuracy, and the effectiveness of the proposed method is proven.
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Võ, Viết Cường, Phuong Hoang Nguyen, Luan Le Duy Nguyen, and Van-Hung Pham. "Econometric Model for Forecasting Electricity Demand of Industry and Construction Sectors in Vietnam to 2030." Science & Technology Development Journal - Engineering and Technology 3, no. 1 (April 5, 2020): First. http://dx.doi.org/10.32508/stdjet.v3i1.646.
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An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the world, and its electricity demand has been increased dramatically high of about 15%/y for the last three decades. Contribution of industry and construction sectors in GDP has been increasing year by year, and are currently holding the leading position of largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate the electricity consumption of these sectors correctly makes a crucial contribution to the planning of the power system. This paper applies an econometric model with Cobb Douglas production function - a top-down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are the value of the sectors in GDP, income per person, the proportion of electricity consumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for long-term forecasting. Variable of electric price does not affect the demand. This is a very critical result of the study for authority governors in Vietnam. In the base scenario of the GDP and the income per person, the forecasted electricity demands of the sectors are 112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high scenario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above values in the high scenario are less than from 9.0% to 15.8 % of that of the based on in the Revised version of master plan N0. VII.
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Ustyugov, Nikita V., and Oleg M. Protalinsky. "Optimizing the Price Category of Electric Power Consumption by an Industrial Enterprise." Vestnik MEI 5, no. 5 (2020): 121–25. http://dx.doi.org/10.24160/1993-6982-2020-5-121-125.
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The purpose of the study is to construct mathematical models of electric energy and power consumption for six price categories in an organizational and technical system and to develop an algorithm based on these models that will enable the consumer to select the most profitable cost of electricity. The system, which was regarded as an integral complex of interacting objects, was analyzed from the viewpoint of a systematic approach. Scientific data were analyzed proceeding from the principles of system consistency, structuring, integrity, hierarchy and multiplicity. A structural-and-functional approach, based on which elements (subsystems), and relationships between them can be considered within a single organizational and technical system, was used as the research method. The current state was studied; the consumption of electricity by an organizational and technical system was predicted based on the source data; the mathematical models of electric energy and power were constructed for six price categories, and a price category selection algorithm was developed, using which the most financially profitable price category can be found. An independent experimental verification of the price category selection algorithm was carried out, which has shown that owing to its application, the cost item “payment of consumed electric energy” for the facility has decreased by 9%. The experiment has shown that the developed mathematical models can be applied in practice, and that the use of the electric energy price category selection algorithm allows economic gains to be obtained. The accomplished study has yielded results based on which the consumers located in the Russian Federation territory can select the most financially profitable price category and cost of electric energy.
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Kim, Ji-Yoon, and Jin-Seok Oh. "Electric Consumption Forecast for Ships Using Multivariate Bayesian Optimization-SE-CNN-LSTM." Journal of Marine Science and Engineering 11, no. 2 (January 30, 2023): 292. http://dx.doi.org/10.3390/jmse11020292.
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Many studies on reducing greenhouse gas emissions from ships have been conducted to reduce environmental pollution. Reducing the fuel oil consumption of traditional and green ships is a key focus of these studies. The fuel oil consumption of the ship depends on electric loads. Thus, ship power load estimation is necessary to develop methods for reducing the fuel oil consumption of ships. However, data accessibility for ship power load estimation is low, limiting the number of relevant studies. This study proposes a model for estimating the actual power load of ships using squeeze and excitation (SE), a convolutional neural network (CNN), and long short-term memory (LSTM). The electric load, power generated by the generator, power consumption of the reefer container, rudder angle, water speed, wind speed, and wind angle of a ship were measured in 10-minute increments for approximately 145 d. The existing parallel and direct CNN-LSTM power load estimation models were used to evaluate the performance of the proposed model. The proposed model had the lowest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), demonstrating the best ship power load estimation performance compared to existing power load estimation models.
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Dhakal, Thakur, and Kyoung-Soon Min. "Macro Study of Global Electric Vehicle Expansion." Foresight and STI Governance 15, no. 1 (March 25, 2021): 67–73. http://dx.doi.org/10.17323/2500-2597.2021.1.67.73.
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This study analyzes the diffusion of battery electric vehicles (BEV) in the world and evaluates the vehicle charging stations based on the European Union (EU) scenario. Initially, the global BEV sales data from 2005 to 2018 are fitted with the two most frequently used econometric logistics and Bass diffusion models. Further, the study identifies the different stage adopters, forecasts the consumption of BEVs, and examines the velocity and acceleration of BEV diffusion. Finally, future charging stations are examined to meet the BEV sales demand. Results suggest that the adoption of BEVs demonstrates a better fit on the Bass model where the global BEV market is estimated to grow from 5,3 millions in 2019 to near 40 millions units by 2030, and with the reference of the EU countries’ adoption scenario, the global charging stations will be increased from near 2 millions in 2019 to near 10 millions units by 2030.
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Kalinchyk, Vasyl, Vitaliy Pobigaylo, Vitaliy Kalinchyk, Aleksandr Meita, and Olena Borychenko. "Combined models of electricity consumption." Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice, no. 1 (7) (June 30, 2022): 34–37. http://dx.doi.org/10.20998/2079-3944.2022.1.07.
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The article investigates models and methods of electric load forecasting. It is shown that among the known methods of power consumption management, preference is given to those based on the use of forecast estimates. The analysis of works devoted to the issues of forecasting the processes of power consumption management systems of industrial enterprises is carried out. It is shown that it is expedient to use adaptive models as a basis for operative forecasting of loads of power supply systems of industrial enterprises. Analysis of adaptive models of electricity consumption forecasting based on the method of exponential smoothing showed their high efficiency and good adaptability to changes in the process of electricity consumption. It is shown that the greatest difficulty in forecasting are cases of abrupt changes in the development of the process. Abrupt changes in the process can lead to a violation of pre-existing qualitative relationships of the parameters of the projected system. If the jump is the transition of the predicted system from one steady state to another, the model of exponential smoothing with correction of the constant smoothing has the best adaptability to this kind of change. At the same time, changes of the "pulse" type are worked out by the model with a certain delay, which leads to an increase in the standard error of the forecast. Therefore, the model's response to change slows down. To eliminate this circumstance, a forecasting procedure based on combined models is proposed. The paper considers two models of combined forecasting - a combined model of joint processing of forecasting results and a combined model of selective type. Experimental studies of the considered models are carried out.
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Samukawa, Tetsuo, Kazuki Shimomoto, and Haruhiko Suwa. "Estimation of In-Process Power Consumption in Face Milling by Specific Energy Consumption Models." International Journal of Automation Technology 14, no. 6 (November 5, 2020): 951–58. http://dx.doi.org/10.20965/ijat.2020.p0951.
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Prediction of energy consumption in the entire production system is crucial for managing production and pursuing environmentally friendly manufacturing. One critical issue that must be addressed to realize green manufacturing is to construct a method for predicting the electric power consumed by each manufacturing device. To address this problem, we have proposed a regression-based power consumption model to predict in-process power consumption based on the strong correlation between MRR and SEC. This study is an extension of our previous work, and here, we conducted face milling experiments by utilizing ten different materials to demonstrate the applicability and generalization capability of the model. We focused on the face milling process and measured the power consumption of the machine tool during the milling process. We also determined the characteristics of the in-process power consumption in face milling from the viewpoint of SEC and MRR and the influence of the work material on SEC. The prediction accuracy of the proposed model is demonstrated by comparison with a conventional model. It was revealed that the proposed model can describe the influence of the entire machine tool on power consumption depending on the characteristics of the work materials.
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Meng, Ming, Wei Shang, and Dongxiao Niu. "Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/243171.
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Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of the monthly electric energy consumption can be obtained by adding the forecasting values of each model. To test the performance by comparison, the proposed and other three models are used to forecast China's monthly electric energy consumption from January 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms of mean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE).
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Klyuev, Roman V., Igor I. Bosikov, Oksana A. Gavrina, and Vladimir Ch Revazov. "System analysis of power consumption by nonferrous metallurgy enterprises on the basis of rank modeling of individual technocenosis castes." MATEC Web of Conferences 226 (2018): 04018. http://dx.doi.org/10.1051/matecconf/201822604018.
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To increase energy efficiency at non-ferrous metallurgy enterprises, an integrated system approach for estimation of electricity consumption is needed. The paper presents the results of a rank analysis of the power consumption of individual castes of process equipment on the basis of an integrated energy survey of the enterprise. A methodology for constructing mathematical models for calculating and predicting electric power consumption for all castes of the ranked H-distribution of technocenosis has been developed. For the first time, according to the established regularity of the H-distribution, a mathematical model for predicting power consumption has been developed, including a quantitative analysis of the energy characteristics of consumers by individual castes of technocenosis. A retrospective check of the relative error in the prediction of electricity consumption showed that for the model it does not exceed 2%, which is significantly lower than the relative error of the prediction for a number of models of other types. The received model is recommended for use in the automated system of dispatching control of power consumption for the purposes of short-term forecasting of electric power consumption at industrial enterprises of non-ferrous metallurgy.
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Bitimanova, Saltanat Serikbaevna, and Asel Asylbekovna Abdildaeva. "Algorithm for optimal control of electric power systems." Bulletin of Toraighyrov University. Energetics series, no. 4.2020 (December 17, 2020): 78–91. http://dx.doi.org/10.48081/wddo6475.
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This paper provides information about the current state of the energy system in Kazakhstan. Also, analyzing the technical condition of the structure of the Kazakhstan electro power station, a mathematical model for complex power systems is developed. Algorithms of control with Adams-Bashforth multistep method are developed. There has been conducted the analysis and assessment of significant factors affecting the forecasted dynamics of electric power consumption, built based on multivariate regression and cointegration models.
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Sun, Zijing, Qunzhi Zhu, Zaiguo Fu, Xi Tian, Changhai Yang, Yong Wei, and Zhengying Liu. "Electric Heating System of Residences without Central Heating: Power Supply Optimization." Journal of Physics: Conference Series 2534, no. 1 (June 1, 2023): 012013. http://dx.doi.org/10.1088/1742-6596/2534/1/012013.
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Abstract For conservation and environmental purposes, three electricity consumption models, power grid, power grid + PV and power grid + PV + battery, are proposed in this paper, the optimization of the electric heating energy consumption model is carried out with the objective of the highest annual net return of the system. The results showed that for the single-layer residence with a housing area of 100m2, the use of the power grid + photovoltaic system has the highest annual net income, as well as good economic and environmental benefits.
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Gu, Qiang, and Xiu Sheng Cheng. "Electric Vehicle Transmission Gear Ratio Optimization Based on Particle Swarm Optimization." Applied Mechanics and Materials 187 (June 2012): 20–26. http://dx.doi.org/10.4028/www.scientific.net/amm.187.20.
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The driving range of electric vehicles is less than traditional vehicles due to the restriction of energy storage. It is raising the efficiency of each power component that is one of increasing electric vehicle driving range methods. A particle swarm optimization is used to optimize transmission gear ratio on established electric vehicle power component models. A simulation that simulates the energy consumption of vehicle after gear ratio optimization is given to compare with the actual energy consumption data of the vehicle before gear ratio optimization. The results show that the energy consumption and driving range of the latter are better than the former therefore this optimization is valid.
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Gerossier, Alexis, Robin Girard, and George Kariniotakis. "Modeling and Forecasting Electric Vehicle Consumption Profiles." Energies 12, no. 7 (April 8, 2019): 1341. http://dx.doi.org/10.3390/en12071341.
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The growing number of electric vehicles (EV) is challenging the traditional distribution grid with a new set of consumption curves. We employ information from individual meters at charging stations that record the power drawn by an EV at high temporal resolution (i.e., every minute) to analyze and model charging habits. We identify five types of batteries that determine the power an EV draws from the grid and its maximal capacity. In parallel, we identify four main clusters of charging habits. Charging habit models are then used for forecasting at short and long horizons. We start by forecasting day-ahead consumption scenarios for a single EV. By summing scenarios for a fleet of EVs, we obtain probabilistic forecasts of the aggregated load, and observe that our bottom-up approach performs similarly to a machine-learning technique that directly forecasts the aggregated load. Secondly, we assess the expected impact of the additional EVs on the grid by 2030, assuming that future charging habits follow current behavior. Although the overall load logically increases, the shape of the load is marginally modified, showing that the current network seems fairly well-suited to this evolution.
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Karshibayev, Asqar I., and Zavqiyor I. Jumayev. "Expanding the level of forecasting and operational planning of electric consumption at mining enterprise." E3S Web of Conferences 417 (2023): 03015. http://dx.doi.org/10.1051/e3sconf/202341703015.
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To calculate the energy consumption of open pits or enterprise as a whole without determining all components of the specific consumption of electricity it is advisable to use a method based on the use of models of power consumption regimes found by the results of multivariate regression analysis. The results of research can serve as a basis for making recommendations for increasing the level of forecasting and operational planning of power consumption in mining enterprises.
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Kassem, Sameh A., Abdulla H. A. EBRAHIM, Abdulla M. Khasan, and Alla G. Logacheva. "FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 1 (2021): 177–93. http://dx.doi.org/10.21684/2411-7978-2021-7-1-177-193.
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Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today. This article discusses the possibility of using artificial neural networks for medium-term forecasting of the power consumption of an enterprise. The task of constructing an artificial neural network using a feedback algorithm for training a network based on the Matlab mathematical package has been implemented. The authors have analyzed such characteristics as parameter setting, implementation complexity, learning rate, convergence of the result, forecasting accuracy, and stability. The results obtained led to the conclusion that the feedback algorithm is well suited for medium-term forecasting of power consumption.