Affiliations: [a] Department of Electronics and Communication Engineering, North China Electric Power University – Baoding, Baoding, Hebei, China | [b] Hebei Key Laboratory of Electric Power Internet of Things Technology, North China Electric Power University, Baoding, Hebei, China
Correspondence:
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Corresponding author: Xiaorui Hu, Department of Electronics and Communication Engineering, North China Electric Power University (Baoding), Baoding, Hebei 071003, China. E-mail: hxr9121@126.com.
Abstract: In order to solve the problems of monthly electricity generation forecasting being limited by the lack of actual data source, and the large errors caused by the influence of various factors such as weather and holidays, and the limitations of the applicable scenarios of the existing research results, a monthly electricity generation forecasting model based on similar month screening and Seasonal and Trend decomposition using Loess (STL) was proposed in this paper. The complementary advantages of Multiple Linear Regression (MLR) and Improved Random Forest Regression (RFR) are utilized to achieve the monthly electricity generation prediction in the province. This prediction model does not require a large number of data to obtain a better prediction accuracy, and breaks through the limitations of the existing monthly electricity prediction model that are only suitable for a certain industry or a certain region. Experiments performed on an actual electric power generation series validate the efficiency of the proposed model.
Keywords: Monthly electricity generation forecast, STL decomposition, similar month screening, multiple linear regression, random forest
