Regional short-term load forecasting method based on power load characteristics of different industries

Abstract

With the gradual increase in the proportion of end-user electricity consumption, the electrical load has become a crucial issue in carbon emission studies. Precise load forecasting will benefit the treatment, calculations, and markets of carbon emission. To explore the characteristics of industrial power load in depth and improve the accuracy of regional short-term load forecasting, this paper proposes a regional short-term load forecasting method based on the power load characteristics of different industries. First, a power load characteristic index system is established to obtain a fine industry classification. Considering the influence of external factors on the power load, the industries are then further classified depending on their sensitivity to external factors using a combination of grey correlation analyses. For the non-external factor-sensitive industries, an integrated model based on the Shapley value method is adopted; for the external factor-sensitive industries, a factor analysis-bidirectional gated recurrent unit-attention mechanism model is proposed. The final combination of the two is the output of the total regional load forecasting results. The combined forecasting method is tested using power load data from a city in Liaoning, China. The results show that the proposed method has apparent advantages over the traditional single forecasting model in terms of forecasting accuracy.