Research on Ultra-short-term combination forecasting algorithm of power load based on machine learning

Abstract

Power load forecasting is of great significance to the power grid marketing department. To obtain accurate load forecasting results, a minute-by-minute forecasting method for electricity load based on multi-stage is proposed (TPE-WXL) by combining the non-linear and time-series attributes. Firstly, the historical series of specific areas in the city are pre-processed. Then, in order to obtain accurately predicted results, XGBoost and LightGBM are applied to extract attributes from the series to build a hybrid model. Moreover, TPE is introduced to enhance the hyperparameters of the hybrid model and the attributes of the load series. Finally, the power load dataset of the region is used as an example to conduct experimental analysis. Experimental results revealed that the method can accurately forecast the trend of power load, that is, R2 =0.981, RMSE=2.643.