Gotowe bibliografie tematyczne / Electric power consumption – econometric models

Gotowa bibliografia na temat „Electric power consumption – econometric models”

Autor: Grafiati

Data publikacji: 27 lipca 2024

Data aktualizacji: 28 lipca 2024

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Artykuły w czasopismach na temat "Electric power consumption – econometric models"

Karpenko, S. M., N. V. Karpenko i G. Y. Bezginov. "Forecasting of power consumption at mining enterprises using statistical methods". Mining Industry Journal (Gornay Promishlennost), nr 1/2022 (15.03.2022): 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 i Tchinda René. "Prediction of electrical energy consumption in Cameroon through econometric models". Electric Power Systems Research 210 (wrzesień 2022): 108102. http://dx.doi.org/10.1016/j.epsr.2022.108102.

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Shin, Sun-Youn, i Han-Gyun Woo. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms". Energies 15, nr 13 (2.07.2022): 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, i Tomasz Pisula. "Forecasting Household Energy Consumption in European Union Countries: An Econometric Modelling Approach". Energies 16, nr 14 (23.07.2023): 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 i Jin Young Kim. "Stride-TCN for Energy Consumption Forecasting and Its Optimization". Applied Sciences 12, nr 19 (20.09.2022): 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 i 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, nr 16 (20.08.2021): 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 i Andrés E. Feijóo-Lorenzo. "Forecasting different dimensions of liquidity in the intraday electricity markets: A review". AIMS Energy 11, nr 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 i Houyin Long. "Regional Heterogeneity Analysis of Residential Electricity Consumption in Chinese Cities: Based on Spatial Measurement Models". Energies 16, nr 23 (30.11.2023): 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 i 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 (27.02.2023): 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, i Błażej Suproń. "Relationship among Economic Growth, Energy Consumption, CO2 Emission, and Urbanization: An Econometric Perspective Analysis". Energies 15, nr 24 (19.12.2022): 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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Rozprawy doktorskie na temat "Electric power consumption – econometric models"

Lee, Cheuk-wing, i 李卓穎. "Transmission expansion planning in a restructured electricity market". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38959410.

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Yan, Yonghe, i 嚴勇河. "A multi-agent based approach to transmission cost allocation". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B3124256X.

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Nyulu, Thandekile. "Weather neutral models for short-term electricity demand forecasting". Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1018751.

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Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.

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Enzinger, Sharn Emma 1973. "The economic impact of greenhouse policy upon the Australian electricity industry : an applied general equilibrium analysis". Monash University, Centre of Policy Studies, 2001. http://arrow.monash.edu.au/hdl/1959.1/8383.

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Fachrizal, Reza. "Synergy between Residential Electric Vehicle Charging and Photovoltaic Power Generation through Smart Charging Schemes : Models for Self-Consumption and Hosting Capacity Assessments". Licentiate thesis, Uppsala universitet, Byggteknik och byggd miljö, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-419665.

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The world is now in a transition towards a more sustainable future. Actions to reduce the green-house gases (GHG) emissions have been promoted and implemented globally, including switching to electric vehicles (EVs) and renewable energy technologies, such as solar photovoltaics (PV). This has led to a massive increase of EVs and PV adoption worldwide in the recent decade. However, large integration of EVs and PV in buildings and electricity distribution systems pose new challenges such as increased peak loads, power mismatch, component overloading, and voltage violations, etc. Improved synergy between EVs, PV and other building electricity load can overcome these challenges. Coordinated charging of EVs, or so-called EV smart charging, is believed to a promising solution to improve the synergy. This licentiate thesis investigates the synergy between residential EV charging and PV generation with the application of EV smart charging schemes. The investigation in this thesis was carried out on the individual building, community and distribution grid levels. Smart charging models with an objective to reduce the net-load (load - generation) variability in residential buildings were developed and simulated. Reducing the net-load variability implies both reducing the peak loads and increasing the self-consumption of local generation, which will also lead to improved power grid performance. Combined PV-EV grid hosting capacity was also assessed. Results show that smart charging schemes could improve the PV self-consumption and reduce the peak loads in buildings with EVs and PV systems. The PV self-consumption could be increased up to 8.7% and the peak load could be reduced down to 50%. The limited improvement on self-consumption was due to low EV availability at homes during midday when the solar power peaks. Results also show that EV smart charging could improve the grid performance such as reduce the grid losses and voltage violation occurrences. The smart charging schemes improve the grid hosting capacity for EVs significantly and for PV slightly. It can also be concluded that there was a slight positive correlation between PV and EV hosting capacity in the case of residential electricity distribution grids.

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Iinuma, Yoshiki. "Scale economies, technological change and capacity factor an economic analysis of thermal power generation in Japan /". Thesis, 1991. http://catalog.hathitrust.org/api/volumes/oclc/27161958.html.

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Ravele, Thakhani. "Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions". Diss., 2018. http://hdl.handle.net/11602/1165.

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MSc (Statistics)
Department of Statistics
Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electricity demand at 19:00 and 20:00 including daily peak electricity demand. Least absolute shrinkage and selection operator (Lasso) and Lasso via hierarchical interactions were used for variable selection to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, this way also over tting is reduced. The parameters of the developed models were estimated using restricted maximum likelihood and penalized regression. The best models were selected based on smallest values of the Akaike information criterion (AIC), Bayesian information criterion (BIC) and Generalized cross validation (GCV) along with the highest Adjusted R2. Forecasts from best models with and without tensor product interactions were evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). Operational forecasting was proposed to forecast the demand at hour 19:00 with unknown predictor variables. Empirical results from this study show that modelling hours individually during the peak period results in more accurate peak forecasts compared to forecasting daily peak electricity demand. The performance of the proposed models for hour 19:00 were compared and the generalized additive model with tensor product interactions was found to be the best tting model.
NRF

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Thanyani, Maduvhahafani. "Forecasting hourly electricity demand in South Africa using machine learning models". Diss., 2020. http://hdl.handle.net/11602/1595.

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MSc (Statistics)
Department of Statistics
Short-term load forecasting in South Africa using machine learning and statistical models is discussed in this study. The research is focused on carrying out a comparative analysis in forecasting hourly electricity demand. This study was carried out using South Africa’s aggregated hourly load data from Eskom. The comparison is carried out in this study using support vector regression (SVR), stochastic gradient boosting (SGB), artificial neural networks (NN) with generalized additive model (GAM) as a benchmark model in forecasting hourly electricity demand. In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso). The SGB model yielded the least root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on testing data. SGB model also yielded the least RMSE, MAE and MAPE on training data. Forecast combination of the models’ forecasts is done using convex combination and quantile regres- sion averaging (QRA). The QRA was found to be the best forecast combination model ibased on the RMSE, MAE and MAPE.
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CHEN, SHANG-YI, i 陳尚毅. "Development of the models and controls of community microgrids with PV and battery energy storage for the assessment of residential-type users’ electric power consumption". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/z2e56v.

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碩士
國立中正大學
電機工程研究所
105
Domestic energy consumption is mainly divided into industrial power, agricultural power, commercial power and residential power, etc. Economic development, climatic factors and population size are common factors which influence the energy consumption and bring on gradual increase of energy consumption. In the economy, the development of heavy industry is required to consume so much energy that the energy consumption increases as well. People's daily life is also closely related to energy consumption. For the general residential users, the gradual increase in load demand will lead to power outage crisis. Therefore, the above problems can be reduced after the community-based micro-grid which is composed of renewable energy generation, storage and control system incorporated into the system. Furthermore, the community-based micro-grid not only can monitor the load demand and power supply but also it can save customers money and utilize energy more efficiently at the same time. To save the cost of testing on physical system, we could verify the feasibility of the proposed method through the system simulations. This thesis analyzes the cases based on actual community-based micro-grid system with construction of renewable energy sources, storage system and controller models and proposes some controlling strategies. Moreover, real-time simulation techniques are used to resolve limitations of off-line simulations and simulation analysis is implemented in condition of grid mode and island mode and limiting power, etc. In the thesis, load forecasting is also executed to extend the functions of simulation system. With the implementation of system simulations, the results show that it not only brings economic benefit for customers but also validate the efficiency of the proposed methods and controlling strategies.

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Książki na temat "Electric power consumption – econometric models"

Losembe, Remy Bolito. Les dépenses en électricité à Kinshasa: Une étude empirique (cas des ménages). Kinshasa, RDC: I.R.E.S., 2004.

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Kulindwa, Kassim. Residential electricity in Tanzania: The case of Dar es Salaam. [Dar es Salaam]: University of Dar es Salaam, Economic Research Bureau, 1996.

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M, Bolet Adela, i Georgetown University. Center for Strategic and International Studies., red. Forecasting U.S. electricity demand: Trends and methodologies. Boulder: Westview Press, 1985.

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Dollinger, Manfred. Eine ökonometrische Analyse der Elektrizitätsnachfrage und der Elektrizitätsproduktion in der Bundesrepublik Deutschland. Idstein: Schulz-Kirchner, 1988.

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Kokkelenberg, Edward Charles. Oil shocks and the demand for electricity. Ithaca, N.Y: Dept. of Agricultural Economics, New York State College of Agriculture and Life Sciences, Cornell University, 1992.

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David, Hawdon, red. Energy demand: Evidence and expectations. London: Surrey University Press, 1992.

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Lin, Bo Q. Electricity demand in the People's Republic of China: Investment requirement and environmental impact. Manila: Asian Development Bank, 2003.

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Lin, Bo Q. Electricity demand in the People's Republic of China: Investment requirement and environmental impact. Manila, Philippines: Asian Development Bank, 2003.

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W, Gellings Clark, i Barron W. L, red. Demand forecasting for electric utilities. Lilburn, GA: Fairmont Press, 1992.

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Veiderpass, Ann. Swedish retail electricity distribution: A non-parametric approach to efficiency and productivity change. Göteborg: [Gothenburg School of Economics?], 1993.

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Części książek na temat "Electric power consumption – econometric models"

Stütz, Sebastian, Andreas Gade i Daniela Kirsch. "Promoting Zero-Emission Urban Logistics: Efficient Use of Electric Trucks Through Intelligent Range Estimation". W iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 91–102. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_8.

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AbstractCritical success factors for the efficient use of electric trucks are the operational range and the total costs of ownership. For both range and efficient use, power consumption is the key factor. Increasing precision in forecasting power consumption and, hence, maximum range will pave the way for efficient vehicle deployment. However, not only electric trucks are scarce, but also is knowledge with respect to what these vehicles are actually technically capable of. Therefore, this article focuses on power consumption and range of electric vehicles. Following a discussion on how current research handles the mileage of electric vehicles, the article illustrates how to find simple yet robust and precise models to predict power consumption and range by using basic parameters from transport planning only. In the paper, we argue that the precision of range and consumption estimates can be substantially improved compared to common approaches which usually posit a proportional relationship between energy consumption and travel distance and require substantial safety buffers.

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Kulakova, Ekaterina, Vadim Kushnikov, Andrey Lazarev i Inessa Borodich. "Models for Determining the Electric Power Consumption in the Water Recycling System at an Industrial Enterprise". W Recent Research in Control Engineering and Decision Making, 378–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65283-8_31.

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Pleskach, Borys. "Estimation of Hidden Energy Losses". W Electric Power Conversion [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97504.

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Most industrial or municipal energy consumers involve the conversion of electricity, either into useful products or into other types of energy. For example, lighting systems, heating systems, air or water supply systems. And in all such systems there are energy losses, which can be divided into open, or technological and hidden, or abnormal. Open losses are inherent in the technological process itself and depend on the principle of energy conversion, flow conditions, the type of equipment received, and so on. Hidden losses in the technological system occur accidentally due to the appearance of defects in the equipment, erroneous actions of personnel, changes in uncontrolled external conditions. The paper considers a method of detecting and estimating hidden energy losses, based on the analysis of energy consumption precedents and building a decision support system aimed at eliminating such energy losses. Models of energy consumption precedents are formed on the basis of controlled technological parameters and their statistical estimates. In the future, local standards of efficient energy consumption are formed from individual precedents. The advantage of this method of estimating latent energy losses is the adaptation of standards of efficient energy consumption to the conditions of the consumer.

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Ashok Shivarkar, Sandip, i Sandeep Malik. "A Survey on Electric Power Demand Forecasting". W Recent Trends in Intensive Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210236.

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Recently there has been tremendous change in use of the forecasting techniques due to the increase in availability of the power generation systems and the consumption of the electricity by different utilities. In the field of power generation and consumption it is important to have the accurate forecasting model to avoid the different losses. With the current development in the era of smart grids, it integrates electric power generation, demand and the storage, which requires more accurate and precise demand and generation forecasting techniques. This paper relates the most relevant studies on electric power demand forecasting, and presents the different models. This paper proposes a novel approach using machine learning for electric power demand forecasting.

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Vergara, Gonzalo, Juan J. Carrasco, Jesus Martínez-Gómez, Manuel Domínguez, José A. Gámez i Emilio Soria-Olivas. "Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings". W Advances in Computer and Electrical Engineering, 207–34. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch011.

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The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of León (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards, we applied state of the art machine learning methods and compare between them. Finally, we predicted daily electric power consumption profiles and compare them with actual data for different buildings. Our analysis shows that multilayer perceptrons have the lowest error followed by support vector regression and clustered extreme learning machines. We also analyze daily load profiles on weekdays and weekends for different buildings.

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Vergara, Gonzalo, Juan J. Carrasco, Jesus Martínez-Gómez, Manuel Domínguez, José A. Gámez i Emilio Soria-Olivas. "Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings". W Architecture and Design, 506–36. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7314-2.ch018.

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Mado, Ismit. "Electric Load Forecasting an Application of Cluster Models Based on Double Seasonal Pattern Time Series Analysis". W Forecasting in Mathematics - Recent Advances, New Perspectives and Applications [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93493.

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Electricity consumption always changes according to need. This pattern deserves serious attention. Where the electric power generation must be balanced with the demand for electric power on the load side. It is necessary to predict and classify loads to maintain reliable power generation stability. This research proposes a method of forecasting electric loads with double seasonal patterns and classifies electric loads as a cluster group. Double seasonal pattern forecasting fits perfectly with fluctuating loads. Meanwhile, the load cluster pattern is intended to classify seasonal trends in a certain period. The first objective of this research is to propose DSARIMA to predict electric load. Furthermore, the results of the load prediction are used as electrical load clustering data through a descriptive analytical approach. The best model DSARIMA forecasting is ([1, 2, 5, 6, 7, 11, 16, 18, 35, 46], 1, [1, 3, 13, 21, 27, 46]) (1, 1, 1)48 (0, 0, 1)336 with a MAPE of 1.56 percent. The cluster pattern consists of four groups with a range of intervals between the minimum and maximum data values divided by the quartile. The presentation of this research data is based on data on the consumption of electricity loads every half hour at the Generating Unit, the National Electricity Company in Gresik City, Indonesia.

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Perez-Moscote, Daniel Adrian, i Mikhail Georgievich Tyagunov. "Improved Distributed Energy Systems Based on the End-User Consumption Profile". W Handbook of Research on Smart Technology Models for Business and Industry, 211–35. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3645-2.ch009.

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Nations are facing today the transition to cleaner, more reliable and affordable energy systems where power grids are becoming less centralized, more flexible and digitalized, and where not only power utilities, but also consumers are playing a significant role. Distributed Energy Systems (DES) constitute a key element in such transition, with decentralized renewable energy generation near the consumption points, energy storage, electric vehicles, and energy management systems, with the potential to ensure continuous supply and achieve higher efficiency, while reducing costs and adverse environmental impacts. This chapter presents a review of the recent advances in the design and development of DES, focusing on the effect of taking into consideration the consumption profile and behaviour of the end-users. The chapter also revises the limitations of DES and summarizes the future directions of DES development.

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Dhupia, Bhawna, i M. Usha Rani. "Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector". W Advances in Wireless Technologies and Telecommunication, 206–18. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7685-4.ch016.

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Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).

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Corcau, Jenica-Ileana, Liviu Dinca i Ciprian-Marius Larco. "Modeling and Simulation of APU Based on PEMFC for More Electric Aircraft". W Aeronautics - New Advances [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105597.

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The current challenge in aviation is to reduce the impact on the environment by reducing fuel consumption and emissions, especially NOX. An open research direction to achieve these desideratums is the realization of new electric power sources based on nonpolluting fuels, a solution being constituted using fuel cells with H2. Reducing the impact on the environment is aimed at both onboard and aerodrome equipment. This paper proposes the simulation and analysis of an auxiliary power source APU based on a fuel cell. The auxiliary power source APU is a hybrid system based on a PEM-type fuel cell, a lithium-ion battery, and their associated converters. The paper presents theoretical models and numerical simulations for each component. The numerical simulation is performed in MATLAB/SimPower Sys. Particular attention is to the converter system that adapts the parameters of the energy sources to the requirements of the electricity consumers on board the MEA-type aircraft. Power management is performed by a controller based on fuzzy logic.

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Streszczenia konferencji na temat "Electric power consumption – econometric models"

Hobby, John D. "Constructing Demand Response Models for Electric Power Consumption". W 2010 1st IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2010. http://dx.doi.org/10.1109/smartgrid.2010.5622075.

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Abdelfattah, Eman, i Kevin Bowlyn. "Application of Machine Learning Models on Individual Household Electric Power Consumption". W 2023 IEEE World AI IoT Congress (AIIoT). IEEE, 2023. http://dx.doi.org/10.1109/aiiot58121.2023.10174456.

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Klavsuts, Irina L., Georgy L. Rusin i Marina V. Khayrullina. "Strategic models of introducing innovative technology for management of electric power consumption into world markets". W 2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2016. http://dx.doi.org/10.1109/apeie.2016.7807064.

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Carlos Da Silva, Daniel, Laid Kefsi i Antonio Sciarretta. "Analytical Models for the Sizing Optimization of Fuel Cell Hybrid Electric Vehicle Powertrains". W 16th International Conference on Engines & Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-24-0133.

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<div class="section abstract"><div class="htmlview paragraph">Improving the development of electrified vehicles requires finding efficient methods for the component sizing of complex powertrains, since they may require a control optimization (for the energy management) which, when added to the sizing optimization, significantly increases the design space. A methodology to estimate the fuel consumption with a closed-form expression is found in the literature, which can be used to reduce the control/plant co-optimization to a static optimization problem. This approach can be used by either estimating the consumption of an existing powertrain: the descriptive level; or by predicting how the consumption will vary with the sizing parameters of the powertrain components: the predictive level. In previous works, the descriptive level was applied to the Toyota Mirai, a Fuel Cell Hybrid Electric Vehicle, showing that it can be extended to vehicles with a fuel cell system. In the present work, the models required for the predictive level are presented, which allow the actual sizing optimization to be performed. The model coefficients are fitted with data from components of different sizing: the battery pack model is fitted with experimental data from roller test benches, the electric machine model is fitted with numerically generated efficiency maps, and the power electronic models are derived from datasheets of discrete components, which are then integrated into the model of the boost converters and the inverter. Finally, the fuel cell system of the Toyota Mirai 2 is used as a reference system for the model. The validation was done by evaluating the errors introduced by each component model. A MATLAB routine was used to calculate the vehicle consumption on driving cycles using the Equivalent Consumption Minimization Strategy for the energy management. The results show that the proposed models adequately estimates the vehicle consumption through the considered cycles, with all errors remaining below three percent.</div></div>

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Li, Candy Yuan, i Douglas Nelson. "Unified Net Willans Line Model for Estimating the Energy Consumption of Battery Electric Vehicles". W WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0348.

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<div class="section abstract"><div class="htmlview paragraph">Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power using a linear relationship. A base model and three normalized models are developed by fitting the UDDS and HWFET energy consumption test data published by the EPA for all BEVs in the U.S. market. Out of the models analyzed, normalizing by weight performs best with the lowest RMSE values at 0.384 kW, 0.747 kW, and 0.988 kW for predicting the UDDS, HWY, and US06 data points, respectively, and 0.653 kW using the combined data of all three data sets. Consideration of accessory loads at 0.5 kW improves the model normalized by weight and performance by a reduction of over 20% in RMSE for predictions with all data sets combined. Removing outliers in addition to the consideration of accessory loads improves the model normalized by weight and performance by a reduction of over 36% in RMSE for predictions with all data sets combined. Overall, results suggest that a unified net Willans line is largely achievable with accessible energy consumption data on U.S. regulatory cycles.</div></div>

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Dranuta, Diego, i Derek Johnson. "Analysis on Combined Heat and Power and Combined Heat and Power Hybrid Systems for Unconventional Drilling Operations". W ASME 2021 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icef2021-67492.

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Abstract The United States (U.S.) has experienced a natural gas “boom” due to the development of unconventional shale plays, but well development is energy intensive. Operations use electric drilling rigs typically powered by either three high-horsepower diesel engines (HHPDE) or three dedicated natural gas engines (DNGE) and associated generators. From a first law analysis, HHPDEs peak at about 42% efficient at full load, while DNGE peak at about 30%. Most fuel energy is lost as heat rejected by the exhaust and radiators. Concurrently, during cold seasonsor or in cold regions rigs utilize boilers to provide steam throughout the rig to prevent freezing and provide comfort. Our analysis focused on a combined heat power (CHP) approach to improve the utilization factor (UF) of fossil energy consumed during unconventional drilling operations. Engine activity, boiler fuel consumption, and exhaust gas temperatures were recorded during winter drilling of an entire well in the Marcellus shale. Four characteristic activity cycles were extracted from recorded activity to represent four energy consumption scenarios. Exhaust and jacket water heat exchangers (E-HEX, JW-HEX) were designed and simulated, and results were analyzed in 0-D models for the four case scenarios. A 584-kWh hybrid energy management system (HEMS) was also designed and simulated into the model as another method to reduce fossil energy fuel consumption during well development. HHPDE UF improved on average from 35.7% to 55.7% if only E-HEX were used and improved to 72.7% if JW-HEX were also used. DNGE average UF increased from 19.0% to 34.9% using E-HEX only. HEMS utilization improved UF up to an average of 76.9% and 39.1% for HHPDE CHP and DNGE CHP systems, respectively.

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Ozalp, Nesrin. "Utilization of Heat, Power and Recovered Waste Heat for Industrial Processes in the US Chemical Industry". W ASME 2008 2nd International Conference on Energy Sustainability collocated with the Heat Transfer, Fluids Engineering, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/es2008-54120.

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This paper presents energy end-use model of the U.S. Chemical Industry. The model allocates combustible fuel and renewable energy inputs among generic end-uses including intermediate conversions through onsite power and steam generation. Results of this model provide the basis to scale energy process-step models. Two federal databases used to construct energy end-use models are Manufacturing Energy Consumption Survey of the U.S. Energy Information Administration, and the Energy Information Administration’s “EIA-860B: Annual Electric Generator Report”. These databases provide information on energy consumption for each end-use, electricity generation, and recovered waste heat at the prime mover level of detail for each industry on a national scale. Results of the model show that the majority of the fuel input is used directly for the end-uses. Although the rest of the fuel is used to generate steam and power, most of this energy contributes to the end-uses as steam. Therefore, the purpose of fuel consumption at non-utility plants is to run their end-uses. During the course of this study, the most recent U.S. federal energy database available was for the year 1998. Currently, the most recent available U.S. federal energy database is given for the year 2002 based on the data collected from 15,500 establishments.

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Briggs, Ian, Geoffrey McCullough, Stephen Spence, Roy Douglas, Richard O’Shaughnessy, Alister Hanna, Cedric Rouaud i Rachel Seaman. "A Parametric Study of an Exhaust Recovery Turbogenerator on a Diesel-Electric Hybrid Bus". W ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94492.

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The fuel consumption of automotive vehicles has become a prime consideration to manufacturers and operators as fuel prices continue to rise steadily, and legislation governing toxic emissions becomes ever more strict. This is particularly true for bus operators as government fuel subsidies are cut or removed. In an effort to reduce the fuel consumption of a diesel-electric hybrid bus, an exhaust recovery turbogenerator has been selected from a wide ranging literature review as the most appropriate method of recovering some of the wasted heat in the exhaust line. This paper examines the effect on fuel consumption of a turbogenerator applied to a 2.4-litre diesel engine. A validated one-dimensional engine model created using Ricardo WAVE was used as a baseline, and was modified in subsequent models to include a turbogenerator downstream, and in series with, the turbocharger turbine. A fuel consumption map of the modified engine was produced, and an in-house simulation tool was then used to examine the fuel economy benefit delivered by the turbogenerator on a bus operating on various drive-cycles. A parametric study is presented which examined the performance of turbogenerators of various size and power output. The operating strategy of the turbogenerator was also discussed with a view to maximising turbine efficiency at each operating point. The performance of the existing turbocharger on the hybrid bus was also investigated; both the compressor and turbine were optimised and the subsequent benefits to the fuel consumption of the vehicle were shown. The final configuration is then presented and the overall improvement in fuel economy of the hybrid bus was determined over various drive-cycles.

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Podlaski, Meaghan, Farhan Gandhi, Robert Niemiec i Luigi Vanfretti. "Multi-Domain Electric Drivetrain Modeling for UAM-Scale eVTOL Aircraft". W Vertical Flight Society 77th Annual Forum & Technology Display. The Vertical Flight Society, 2021. http://dx.doi.org/10.4050/f-0077-2021-16893.

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This paper presents the modeling and validation of an electric drivetrain through an object-oriented, equation-based framework that includes aerodynamics, electric machine, power electronic converter and battery models at various levels of detail. The proposed drivetrain model considers different losses in the machine and levels of fidelity for the power source and converters. It is simulated with various maneuvers, aiming to show the effects of modeling simplifications on the behavior of UAMs. These studies show that the level of detail in the motors and power system has significant impact on the dynamic response and power consumption of the system. This is most evident in the cases where the system uses a detailed battery model and in the cases where the switching electrical components are used, creating a torque ripple.

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Rakova, Elvira, i Jürgen Weber. "Exonomy Analysis for the Selection of the Most Cost-Effective Pneumatic Drive Solution". W 9th FPNI Ph.D. Symposium on Fluid Power. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/fpni2016-1518.

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Today pneumatic drives are widely used to perform various motion tasks. They distinguish themselves through low purchase price and robust design, but show high energy consumption in comparison with electric drives. Existing energy saving measures lead to the reduction of energy consumption, but at the same time they cause the increase of the life cycle costs. All in all, the selection of pneumatic drives has to be done regarding their functionality, efficiency and costs. In this paper the novel Exonomy approach is presented for the selection of the most cost-effective pneumatic drive solution. Developed analysis enables 3 steps. First step includes the new approach for the sizing of pneumatic cylinders based on exergy-energy balance. The term Sizing Factor (SF) is introduced to perform the grade of over sizing of the actuator due to the loss occurred in the system. The second step provides the information about energy consumption. The last step enables the information about life cycle costs of the system and gains the data about amortization time based on Life Cycle Costs (LCC). In the current study all steps of Exonomy analysis has been applied to the vertical as well as to the horizontal pneumatic drives. This study has identified SF for various loading mass and velocities, typical for handling machines. The simulation models were validated with the help of the measurement results. Summarizing, a new formula is proposed to choose the design parameters of the drive. And finally, the quid-line is presented to choose the most cost-effective drive solution.

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Raporty organizacyjne na temat "Electric power consumption – econometric models"

Haddad, J., L. A. Horta Nogueira, Germano Lambert-Torres i L. E. Borges da Silva. Energy Efficiency and Smart Grids for Low Carbon and Green Growth in Brazil: Knowledge Sharing Forum on Development Experiences: Comparative Experiences of Korea and Latin America and the Caribbean. Inter-American Development Bank, czerwiec 2015. http://dx.doi.org/10.18235/0007001.

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The Brazilian continental dimensions and diversified natural resources are proportional to the challenges to develop its infrastructure sustainably and supply proper public services to more than 200 million inhabitants. Energy consumption has doubled since 1990, fostered by economic growth and the expansion of middle class. In this context, promote energy efficiency, in a broad sense, is urgent and rational. Brazil has a relatively long history in promoting energy efficiency at final user level. A landmark of this process is the Brazilian Labeling Program, launched in 1984, as direct consequence of high prices of energy at that time. This program was coordinated by the National Institute of Metrology, Standardization and Industrial Quality, which sets standards for evaluation, ranks the performance of energy equipment and imposes a classificatory labeling to inform consumers, with a label similar to other countries. The National Electricity Conservation Program was created in 1985 by MME and is executed by ELETROBRÁS. The energy saving induced by this program in 2013 is equivalent to 2.1% of the total electric energy consumption in the period, corresponding to the annual energy consumption of about 5 million Brazilian households. In 2001, Federal Law 10,295, also known as the Energy Efficiency Law, was approved to reinforce those energy efficiency programs, allowing the Brazilian government to establish Minimum Energy Performance Standards for appliances and energy equipment, prohibiting the commercialization of low efficiency models and promoting the progressive withdrawal of low-efficiency models. According to the National Energy Plan 2030, up to 15.5 GW of electricity generation could be saved as a result of energy efficiency in the next 20 years. The Smart Grids, adopting modern technologies in electricity distribution has been proposed in Brazil improve the quality provided in the low voltage service, reduce losses, and reduce operating costs, among others. Several regulations related to this subject, dealing with grid connection for distributed small-scale generation, the establishment of the 'hourly tariff', with the regulation of the use of PLC; and with the compulsory use of Geographic Information System. Currently, dozens pilot projects on Smart Grids are underway in the country. Two projects are presented in detail: CEMIG and AES Eletropaulo, two Brazilian power utilities.

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