Short-Term Load Forecasting Method Based on Multi-model Fusion Using CNN-LSTM-XGBoost Framework

doi:10.11930/j.issn.1004-9649.202004026

Abstract

Abstract: Accurate short-term load forecasting can provide effective guidance for unit scheduling, economic dispatch and power market operations. Concerning the low accuracy problem of load forecasting brought by the limited features of input data, a method based on multi-model fusion using CNN-LSTM-XGBoost framework is proposed. The Long Short-Term Memory network structure fused with local feature pre-extraction module is first established and then integrated with the XGBoost prediction model in parallel. Afterwards by using mean absolute percentage error-reciprocal weight algorithm to set initial model fusion weights and start searching for optimal weight, the optimal fusion model is built. From the prediction experiment of load data by virtual of the proposed method, it is discovered that the mean average percentage error and the root mean squared error of CNN-LSTM-XGBoost are 0.337% and 148.419 MW respectively, which indicates significant decrease of the error metrics compared with the outcome using single network model and multi-model structure. Therefore, it is verified that the method based on multi-model fusion using CNN-LSTM-XGBoost framework has faster training speed, higher accuracy and lower error of prediction.

Key words: short-term load forecasting, local feature pre-extraction, long short-term memory, XGBoost, multi-model fusion

ZHUANG Jiayi, YANG Guohua, ZHENG Haofeng, ZHANG Honghao. Short-Term Load Forecasting Method Based on Multi-model Fusion Using CNN-LSTM-XGBoost Framework[J]. Electric Power, 2021, 54(5): 46-55.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202004026

https://www.electricpower.com.cn/EN/Y2021/V54/I5/46

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