Ultra-short-term load forecasting based on BiLSTM network and error correction

doi:10.3969/j.issn.2097-0706.2023.01.004

Abstract

Abstract:

Accurate power load forecasting is important to maintain the balance of power supply and demand and the economy and stability of power system. Since power load are random and volatile under the influence of meteorological factors，accurate forecasting on power load is a technical challenge. An ultra-short-term load forecasting model is built based on bi-directional long short-term memory （BiLSTM） neural network and error correction. Maximum information coefficient （MIC） is used to describe the nonlinear relationships between the various influencing factors and load data， so as to filter the input characteristics. Considering the time-series characteristics of the load sequence， the initial load forecasting model is established based on the BiLSTM network. To lessen the prediction error， complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）algorithm is used to decompose the error sequence into several error components， whose BiLSTM prediction models are built respectively to modify the initial predicted results. The simulation experiment on a power distribution network in Tianjin， a northern municipality in China， is carried out. The experimental results show that the prediction values made by the BiLSTM-based model is more accurate that made by the models based on other neural networks.

Key words: power load, ultra-short-term load forecasting, BiLSTM neural network, CEEMDAN, error correction, MIC

CLC Number:

TK01：TM715

GAO Ming, HAO Yan. Ultra-short-term load forecasting based on BiLSTM network and error correction[J]. Integrated Intelligent Energy, 2023, 45(1): 31-40.

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影响因素	MIC值
预测日的天气类型	0.21
预测日的最高气温	0.51
预测日的最低气温	0.53
预测日的平均气温	0.48
预测日的日期类型	0.22

模型	δ_MAE/MW	δ_RMSE/MW	δ_MAPE/%
BP	6.72	7.95	6.67
BiLSTM	2.52	3.27	2.41
BiLSTM-BiLSTM	2.49	3.19	2.39
BiLSTM-EMD-BiLSTM	2.37	3.31	2.37
CNN-BiLSTM-Attention	2.09	2.82	2.22
本文方法	1.23	1.49	1.27

模型	δ_MAE/MW	δ_RMSE/MW	δ_MAPE/%
BP	4.50	6.58	2.36
BiLSTM	3.92	6.19	2.05
BiLSTM-BiLSTM	9.16	12.41	4.79
BiLSTM-EMD-BiLSTM	3.79	5.28	1.98
CNN-BiLSTM-Attention	3.11	4.90	1.63
本文方法	2.86	4.83	1.53