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Yingnan Wang
University of Chinese Academy of Science

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Research in Residential Electricity Characteristics and Short-Term Load Forecasting Haixia Feng; Zhongfeng Wang; Weichun Ge; Yingnan Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 12: December 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper we make research in Residential short-term load forecasting. Different application scenes have different affecting factors of short-term load, so we should specifically analysis of factors that affect the load of the residential electricity. We use SPSS (Statistic Package for Social Science) to figure out the relationship between the daily load and temperature, weather conditions and other factors, finding the main factors among the impacting factors, and analyzing residential electricity consumption habits and load characteristics. Then, the paper introduces the common prediction methods. Combining with the above analysis to choose short-term load forecasting methods for residential users, we create automatic linear regression model and artificial neural network model to predict the future electricity load, calculating the residual between the predicted values and the actual values and mean square deviation of the values, and evaluating the accuracy of the load forecasting. The results prove that automatic linear regression model is effective in residential short-term electricity load forecasting. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3740
Co-Authors Haixia Feng Weichun Ge Zhongfeng Wang