Affiliations: [a]
School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang, China
| [b]
School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, China
Correspondence:
[*]
Corresponding author. Xifeng Guo, School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang, 110168, China. Tel.: +86 139 9888 5572; E-mail: [email protected].
Abstract: Accurate load forecasting is an important issue for safe and economic operation of power system. However, load data often has strong non-stationarity, nonlinearity and randomness, which increases the difficulty of load forecasting. To improve the prediction accuracy, a hybrid short-term load forecasting method using load feature extraction based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and refined composite multi-scale entropy (RCMSE) and improved bidirectional long short time memory (BiLSTM) error correction is proposed. Firstly, CEEMDAN is used to separate the detailed information and trend information of the original load series, RCMSE is used to reconstruct the feature information, and Spearman is used to screen the features. Secondly, an improved butterfly optimization algorithm (IBOA) is proposed to optimize BiLSTM, and the reconstructed components are predicted respectively. Finally, an error correction model is constructed to mine the hidden information contained in error sequence. The experimental results show that the MAE, MAPE and RMSE of the proposed method are 645 kW, 0.96% and 827.3 kW respectively, and MAPE is improved by about 10% compared with other hybrid models. Therefore, the proposed method can overcome the problem of inaccurate prediction caused by data and inherent defects of models and improve the prediction accuracy.
Keywords: Short-term load forecasting, complete ensemble empirical mode decomposition with adaptivenoise, refined composite multi-scale entropy, improved butterfly optimization algorithm, bidirectional long short time
memory neural network
