Abstract
Hundreds of explanatory variables such as historical consumption data, climate variables, socioeconomic and demographics parameters, etc. are used to forecast major forms of commercial energy consumptions. The scientists increasingly face the problems of big data: huge amount of data which grows in time. This article presents the analysis of the relationships between electricity amount to deliver and factors having more or less significant impact on electricity consumptions. The linear regression algorithm is used to reduce the set of explanatory variables and to evaluate their importance. A reduction of number of variables without significant loss of accuracy of the model is presented. Next, regression decision trees are used both to evaluate the quality of the modeled energy consumption, as well as to further explanatory variables reduction. The last part of the article deals with the outlining data points and explains reasons they come off model predictions.
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References
Singh, A.K, Ibraheem, S.K., Muazzam, Md.: An overview of electricity demand forecasting techniques. In: Proceedings of National Conference on Emerging Trends in Electrical, Instrumentation & Communication Engineering, vol. 3, No. 3, pp. 38â48 (2013)
Rajan, M., Jain, V.K.: Modelling of electrical energy consumption in Delhi. Energy 24, 351â361 (1999)
Soytas, U., Sari, R.: Energy consumption and GDP: causality relationship in Gâ7 countries and emerging markets. Energy Econ. 3, 33â37 (2003)
Yan, Y.Y.: Climate and residential electricity consumption in Hong Kong. Energy 23(1), 17â20 (1998)
Shiu, A., Lam, P.L.: Electricity consumption and economic growth in China. Energy Policy 32, 47â54 (2004)
Egelioglu, F., Mohamad, A.A., Guven, H.: Economic variables and electricity consumption in Northern Cyprus. Energy 26(4), 355â362 (2001)
Parameswara Sharma, D., Chandramohanan Nair, P.S., Balasubramanian, R.: Demand for commercial energy in the state of Kerala, India: an econometric analysis with medium-range projections. Energy Policy 30, 781â791 (2002)
Yang, M., Yu, X.: Chinaâs rural electricity marketâa quantitative analysis. Energy 29(7), 961â977 (2004)
Gori, F., Takanen, C.: Forecast of energy consumption of industry and household and services in Italy. Heat Technol. 22(2), 115â121 (2004)
Pao, H.T.: Comparing linear and nonlinear forecasts for Taiwanâs electricity consumption. Energy 31, 2129â2141 (2006)
Hor, C.L., Watson, S.J., Majithia, S.: Analyzing the impact of weather variables on monthly electricity demand. IEEE Trans. Power Syst. 20, 2078â2085 (2005)
Giannakopoulos, Ch., Psiloglou, B.E.: Trends in energy load demand for Athens, Greece: weather and non-weather related factors. Clim. Res. 31, 97â108 (2006)
Pardo, A., Meneu, V., Valor, E.: Temperature and seasonality influences on Spanish electricity load. Energy Econ. 24, 55â70 (2002)
Sailor, D.J.: Relating residential and commercial sector electricity loads to climate â evaluating state level sensitivities and vulnerabilities. Energy 26, 645â657 (2001)
Valor, E., Meneu, V., Caselles, V.: Daily air temperature and electricity load in Spain. J. Appl. Meteor. 40, 1413â1421 (2001)
Gajowniczek, K., Nafkha, R., ZÄ bkowski, T.: Electricity peak demand classification with artificial neural networks. Comput. Sci. Inf. Syst., ACSIS 11, 307â315 (2017)
Marcjasz, G., Uniejewski, B., Weron, R.: Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models. HSC Research Reports HSC/17/03 (2017)
Polish power system dataset. http://www.pse.pl/index.php?dzid=77. Accessed 12 Aug 2017
Hocking, R.R.: The analysis and selection of variables in linear regression. Biometrics 32(1), 1â49 (1976)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. CRC Press, Boca Raton (1984)
Rokach, L., Maimon, O.: Data mining with decision trees. Theory and applications. World Scientific Pub Co Inc., Singapore (2008)
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Karpio, K., Ćukasiewicz, P., Nafkha, R. (2019). Regression Technique for Electricity Load Modeling and Outlined Data Points Explanation. In: PejaĆ, J., El Fray, I., Hyla, T., Kacprzyk, J. (eds) Advances in Soft and Hard Computing. ACS 2018. Advances in Intelligent Systems and Computing, vol 889. Springer, Cham. https://doi.org/10.1007/978-3-030-03314-9_5
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