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Academic literature on the topic 'Electric power consumption Regression analysis'
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Journal articles on the topic "Electric power consumption Regression analysis"
1
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 (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.
3
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.
4
Gracheva, E. I., and O. V. Naumov. "Application of fuzzy regression analysis method for determination of electric power losses in intrafactory power supply networks." Safety and Reliability of Power Industry 11, no. 4 (2019): 325–31. http://dx.doi.org/10.24223/1999-5555-2018-11-4-325-331.
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One of the main objectives of the development of modern industry in Russia, along with an increase in the absolute volumes of electric power (EP) production, is to strengthen control over its more rational use. Saving EP and reducing the cost of its transmission along power distribution networks is of great importance for the country's energy sector. In terms of their physical nature, in terms of production, transmission and consumption, EP losses are no different from EP served to consumers. Therefore, the assessment of power losses in electrical networks is based on the same economic principles as the assessment of energy served to consumers. EP losses have a significant impact on the technical and economic parameters of the network, since the cost of losses is included in the estimated cost (reduced costs) and cost price (annual operating costs) of EP transmission. The cost component of losses in the cost of EP transmission has a large proportion. The article presents the results of research on the possibility of application of fuzzy regression analysis for problems of assessment and prediction of electric power losses in intrafactory networks. Initial information on the network is uncertain to some extent, which complicates application of traditional methods. The calculation is presented for conventional and fuzzy regression models, along with estimation of error of these models. The relevance of application of fuzzy regression analysis methods is determined by the difficulty of obtaining reliable information about the circuit and regime parameters of intrafactory networks, the probabilistic nature of change of the modes, as well as a whole complex of affecting factors, which are generally challenging for quantitative assessment. Advantages of application of fuzzy regression analysis consist in obtaining confidence intervals of required variables (value of electric power losses) for schemes of networks with uncertain initial information on their parameters, which is characteristic of intrafactory power supply systems, and enables to consider dynamics of their variation.
5
Istomin, Stanislav Gennadyevich, and Oleg Dmitrievich Yurasov. "Simulation model of heating system of DC electric-multiple units." Transport of the Urals, no. 4 (2020): 75–79. http://dx.doi.org/10.20291/1815-9400-2020-4-75-79.
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Most of the Russian Federation territory is located in the zone of long-term exposure to negative ambient temperatures. In this regard, a significant proportion of power consumption in the suburban traffic on the railways of the Russian Federation accounts for the operation of heating and air conditioning systems. Currently, Russian and foreign scientists are developing energy-saving methods and tools to reduce the power consumption for auxiliary needs of electric-multiple units. In this paper, the authors used the method of constructing simulation models in the MATLAB Simulink program in order to create an energy-saving heating and air conditioning system since this method allows you to explore various options for constructing the studied systems with lower financial and labour costs in comparison with the experimental method. In order to verify its adequacy the simulation model includes standard values of electric energy consumption for heating and air conditioning for various sections and operating conditions obtained by the authors earlier during the correlation and regression analysis of data from parameter recorders installed in electric-multiple units. The results of the study showed the adequacy of application of the developed simulation model for organizing the control of power consumption for heating and air conditioning of direct current electric-multiple units.
6
Baklanov, Alexander, Nikolay Yesin, and Andrey Shilyakov. "ULL AND ENERGY EFFICIENCY ANALYSIS OF NEW ELECTRIC LOCOMOTIVES." Bulletin of scientific research results, no. 4 (December 17, 2017): 70–80. http://dx.doi.org/10.20295/2223-9987-2017-4-70-80.
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Objective: To study the specificities and parameters of the new, including innovative, freight and passenger electric locomotives, produced for domestic railways in the framework of the program of creating the new locomotives in 2004–2010. To analyze pull and energy efficiency parameters of direct current and alternating current electric locomotives. To estimate the maximum weight of trains and specific energy consumption of electric locomotives. To detect the advantages of new electric locomotives in comparison with those produced earlier. To develop guidelines on efficiency improvement of the new electric locomotives. Methods: Comparative analysis, methods of grade computations, linear regression analysis, power balance method. Results: The main design features and parameters of the new and earlier produced electric locomotives were studied, the former include the power of tractive motors, traction effort, as well as the speed at continuous rating of traction. The parameters of the new and earlier produced electric locomotives were compared. Key performance indicators of electric locomotives were analyzed, such as the maximum mass of a train and specific energy consumption on traction. The comparison of the above-mentioned indicators with performance indicators of earlier produced electric locomotives was given. According to calculation data and statistical data analysis the advantages of new electric locomotives were determined over those produced earlier. High performance of regenerative breaking was shown, specifically new electric locomotives. It was detected that in winter regeneration of electric energy was significantly reduced, in case of regenerative braking of passenger electric locomotives series EP1 with alternating current, as most of energy generated by tractive motors was spent on electric heating circuits of passenger cars. Guidelines on efficiency improvement of new electric locomotives were developed. Practical importance: The conditions in which new electric locomotives would implement the available advantages were determined, compared to those produced earlier. The elaborated offers make it possible to improve pull and energy efficiency of the new electric locomotives in operation.
7
Krutsyak, Mykhailo. "FORECASTING DEMAND ON THE DOMESTIC ELECTRICITY MARKET ON THE BASIS OF THE RESULTS OF SOCIAL AND ECONOMIC INDICATORS DYNAMICS ANALYSIS." Economic Analysis, no. 28(3) (2018): 37–46. http://dx.doi.org/10.35774/econa2018.03.037.
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The works, which are devoted to the forecasting of demand for electric power, are analysed in this research. A number of these works is identified in order to use the available data. The influence of individual social and economic factors on the volume of annual electricity consumption in Ukraine is investigated. The use of forecasting of demand for electric energy data on the volume of gross domestic product on the parity of purchasing power, GDP energy intensity and the population of Ukraine for the period of 1991-2017 are substantiated, as well as the correlation between them. The annual volumes of electricity consumption are determined. It has been proposed the economic and mathematical model of forecasting and use of multiple regression equations. The method of reduction of the nonlinearity of the dynamics of the investigated factors is considered. We have compared the results, which are obtained after the use of this model, with the results of the available national forecasts.
8
Istomin, Stanislav, and Aleksandr Shtraukhman. "Simulation model of the heating and air conditioning system of dc electric trains." E3S Web of Conferences 135 (2019): 02018. http://dx.doi.org/10.1051/e3sconf/201913502018.
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Most of the territory of the Russian Federation is located in the zone of long-term exposure to negative ambient temperatures. In this regard, in the suburban traffic on the railways of the Russian Federation, a significant proportion of the electric power falls on the operation of heating and air conditioning systems. Nowadays, Russia and the world are developing energy-saving methods and tools to reduce the energy consumption of auxiliary needs of electric trains. In this paper, the method of constructing simulation models in the MATLAB Simulink software was used to build an energy-saving heating and air conditioning system, since this method allows studying various options for building the studied systems with lower financial and labor costs in comparison with the experimental method. The correct selection and display of the parameters of the electric train interior will allow achieving the optimal values of energy consumption for heating and air conditioning of the electric trains. In order to verify its adequacy, the simulation model includes standard values of electric energy consumption for heating and conditioning electric trains for various sections and operating conditions, which were obtained earlier during the correlation and regression analysis of data from electric train parameter recorders. The results of the study showed the adequacy of the application of the developed simulation model for organizing the control of electric power consumption for heating and air conditioning of DC electric trains.
9
Pokharel, Sugam, Pradip Sah, and Deepak Ganta. "Improved Prediction of Total Energy Consumption and Feature Analysis in Electric Vehicles Using Machine Learning and Shapley Additive Explanations Method." World Electric Vehicle Journal 12, no. 3 (2021): 94. http://dx.doi.org/10.3390/wevj12030094.
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Electric vehicles (EVs) have emerged as the green energy alternative for conventional vehicles. While various governments promote EVs, people feel “range anxiety” because of their limited driving range or charge capacity. A limited number of charging stations are available, which results in a strong demand for predicting energy consumed by EVs. In this paper, machine learning (ML) models such as multiple linear regression (MLR), extreme gradient boosting (XGBoost), and support vector regression (SVR) were used to investigate the total energy consumption (TEC) by the EVs. The independent variables used for the study include changing real-life situations or external parameters, such as trip distance, tire type, driving style, power, odometer reading, EV model, city, motorway, country roads, air conditioning, and park heating. We compared the ML models’ performance along with the error analysis. A pairwise correlation study showed that trip distance has a high correlation coefficient (0.87) with TEC. XGBoost had better prediction accuracy (~92%) or R2 (0.92). Trip distance, power, heating, and odometer reading were the most important features influencing the TEC, identified using the shapley additive explanations method.
10
Soava, Georgeta, Anca Mehedintu, Mihaela Sterpu, and Eugenia Grecu. "The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania." Energies 14, no. 9 (2021): 2394. http://dx.doi.org/10.3390/en14092394.
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This paper analyzes the impact of the COVID-19 pandemic on economic growth and electricity consumption and investigates the hypothesis of the influence of this consumption on the gross domestic product (GDP) for Romania. Using time series on monthly electricity consumption and quarterly GDP and a multi-linear regression model, we performed an analysis of the evolution of these indicators for 2007–2020, a comparison between their behavior during the financial crisis vs. COVID-19 crisis, and empirically explore the relationships between GDP and electricity consumption or some of its components. The results of the analysis confirm that the shock of declining activity due to the COVID-19 pandemic had a severe negative impact on electric energy consumption and GDP in the first half of 2020, followed by a slight recovery. By using a linear regression model, long-term relationships between GDP and domestic and non-household electricity consumptions were found. The empirically estimated elasticity coefficients confirm the more important impact of non-household electricity consumption on GDP compared to the one of domestic electricity consumption. In the context of the COVID-19 pandemic, the results of the study could be useful for optimizing energy and economic growth policies at the national and European levels.