Short-Term Load Forecasting on Individual Consumers

João Victor Jales Melo (), George Rossany Soares Lira, Edson Guedes Costa, Antonio F. Leite Neto and Iago B. Oliveira
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João Victor Jales Melo: Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
George Rossany Soares Lira: Electrical Engineering Department, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
Edson Guedes Costa: Electrical Engineering Department, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
Antonio F. Leite Neto: Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
Iago B. Oliveira: Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil

Energies, 2022, vol. 15, issue 16, 1-16

Abstract: Maintaining stability and control over the electric system requires increasing information about the consumers’ profiling due to changes in the form of electricity generation and consumption. To overcome this trouble, short-term load forecasting (STLF) on individual consumers gained importance in the last years. Nonetheless, predicting the profile of an individual consumer is a difficult task. The main challenge lies in the uncertainty related to the individual consumption profile, which increases forecasting errors. Thus, this paper aims to implement a load predictive model focused on individual consumers taking into account its randomness. For this purpose, a methodology is proposed to determine and select predictive features for individual STLF. The load forecasting of an individual consumer is simulated based on the four main machine learning techniques used in the literature. A 2.73% reduction in the forecast error is obtained after the correct selection of the predictive features. Compared to the baseline model (persistent forecasting method), the error is reduced by up to 19.8%. Among the techniques analyzed, support vector regression (SVR) showed the smallest errors (8.88% and 9.31%).

Keywords: load forecasting; machine learning; neural network; smart meter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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