Research on short-term load forecasting of power system based on gradient lifting tree Online publication date: Sun, 14-May-2023
by Taofang Xia; Yajuan Zhou; Shian Zhan; Hua Lin; Tuo Zhang; Yueqian Lan
International Journal of Power and Energy Conversion (IJPEC), Vol. 13, No. 3/4, 2022
Abstract: To reduce the average absolute error of short-term load forecasting and improve the calculation speed, a short-term load forecasting method of power system based on gradient lifting tree is designed. Firstly, after determining the input of short-term load forecasting, the fuzzy probability is used to quantify the short-term load influencing factors and complete the load data pre-processing. Secondly, the short-term load series are processed by the difference decomposition method. Finally, in the direction of the negative gradient of the loss function, a strong regression gradient lifting tree is established, and the historical short-term load sequence is input into it to obtain the load forecasting results. The experimental results show that the maximum average absolute error of the prediction results of this method is only 1.41%, the minimum prediction calculation speed is 7.5 s, and the maximum prediction calculation speed is only 9.6 s.
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