Research on short-term load forecasting of power system based on gradient lifting tree

被引：0

作者：

Xia T. ^{[1
]}

Zhou Y. ^{[2
]}

Zhan S. ^{[1
]}

Lin H. ^{[1
]}

Zhang T. ^{[3
]}

Lan Y. ^{[2
]}

机构：

[1] State Grid Fujian Marketing Service Centre, Fujian

[2] Energy Internet Research Institute, Tsinghua University, Beijing

[3] China Urban Construction Design and Research Institute Co., Ltd., Beijing

来源：

International Journal of Power and Energy Conversion | 2022年 / 13卷 / 3-4期

关键词：

differential decomposition; gradient lifting tree; load forecasting; power system; pre-treatment; short-term load;

D O I：

10.1504/IJPEC.2022.130951

中图分类号：

学科分类号：

摘要：

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. Copyright © 2022 Inderscience Enterprises Ltd.

引用

页码：235 / 247

页数：12

共 15 条

[1]

Bi Y., Zhang J., Xu X., Sun W., Zhang Z., Short term load forecasting model of power system based on gradient boosting decision tree, Journal of Qingdao University(Engineering & Technology Edition), 33, 3, pp. 70-75, (2018)

[2]

Cai X., Han A., Zhou S., Wei Z., Zhang Z., Power load forecasting based on min peephole long short term memory, Guangdong Electric Power, 34, 3, pp. 92-97, (2021)

[3]

Chen P., Fang Y., Short term load forecasting of power system for holiday point-by-point growth rate based on Kalman filtering, Engineering Journal of Wuhan University, 53, 2, pp. 139-144, (2020)

[4]

Dat N.Q., Ngoc Anh N.T., Nhat Anh N., Solanki V.K., Thampi S.M., El Sayed E.A.M., Trajkovic L., Hybrid online model based multi seasonal decompose for short term electricity load forecasting using ARIMA and online RNN, Journal of Intelligent & Fuzzy Systems, 41, 5, pp. 5639-5652, (2021)

[5]

Feng Q., Qian S., Research on power load forecasting model of economic development zone based on neural network, Energy Reports, 7, S7, pp. 1447-1452, (2021)

[6]

Huang J., Zhou Z., Li C., Liao Z., Liu P.X., A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting, IET Generation, Transmission & Distribution, 15, 24, pp. 3459-3473, (2021)

[7]

Liu J., Yin Y., Power load forecasting considering climate factors based on IPSO-Elman method in China, Energies, 15, 3, pp. 1236-1242, (2022)

[8]

Madhiarasan M., Louzazni M., Different forecasting horizons based performance analysis of electricity load forecasting using multilayer perceptron neural network, Forecasting, 3, 4, pp. 804-838, (2021)

[9]

Saeed F., Paul A., Seo H., A hybrid channel-communication-enabled CNN-LSTM model for electricity load forecasting, Energies, 15, 6, pp. 2263-2271, (2022)

[10]

Tasarruf B., Chen H., Faizan T.M., Zhu L., Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN, Energy Reports, 8, 1, pp. 1678-1686, (2022)

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