Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

Huiqun Yu, Haoyi Sun, Yueze Li, Chunmei Xu () and Chenkun Du
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Huiqun Yu: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Haoyi Sun: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Yueze Li: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Chunmei Xu: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Chenkun Du: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Energies, 2024, vol. 17, issue 21, 1-22

Abstract: To tackle the challenges of high variability and low accuracy in short-term electricity load forecasting, this study introduces an enhanced prediction model that addresses overfitting issues by integrating an error-optimal weighting approach with an improved ensemble forecasting framework. The model employs a hybrid algorithm combining grey relational analysis and radial kernel principal component analysis to preprocess the multi-dimensional input data. It then leverages an ensemble of an optimized deep bidirectional gated recurrent unit (BiGRU), an enhanced long short-term memory (LSTM) network, and an advanced temporal convolutional neural network (TCN) to generate predictions. These predictions are refined using an error-optimal weighting scheme to yield the final forecasts. Furthermore, a Bayesian-optimized Bagging and Extreme Gradient Boosting (XGBoost) ensemble model is applied to minimize prediction errors. Comparative analysis with existing forecasting models demonstrates superior performance, with an average absolute percentage error (MAPE) of 1.05% and a coefficient of determination (R 2 ) of 0.9878. These results not only validate the efficacy of our proposed strategy, but also highlight its potential to enhance the precision of short-term load forecasting, thereby contributing to the stability of power systems and supporting societal production needs.

Keywords: short-term power load forecasting; kernel principal component analysis; sparrow search algorithm; gated recurrent unit; time-domain convolutional networks; long short-term memory; extreme gradient boosting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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