A hybrid framework for short-term load forecasting based on optimized InMetra Boost and BiLSTM

Qinghe Zhao, Shengduo Wang, Yuqi Chen, Jinlong Liu, Yujia Sun, Tong Su, Ningning Li and Junlong Fang

Energy, 2025, vol. 328, issue C

Abstract: Accurate short-term load forecasting is essential for maintaining stable and efficient power grid operations, especially with the increasing complexity introduced by renewable energy sources. This paper proposes a novel hybrid model for day-ahead Short-Term Load Forecasting, combining an improved Boosting algorithm, InMetra Boost, and a Bidirectional Long Short-Term Memory model. InMetra Boost introduces an asymmetric penalty mechanism, allowing for more precise handling of positive and negative forecast deviations. The model's hyperparameters are optimized using the Tree-structured Parzen Estimator, and the temporal dependencies in load data are further captured by a BiLSTM model, whose architecture is refined via the Cuckoo Search algorithm. The proposed TPE-IMB-CS-BiLSTM (Tree-structured Parzen Estimator optimized InMetra Boost within BiLSTM tuned by Cuckoo Search) framework was evaluated on real-world Estonian grid data, demonstrating superior performance compared to traditional models. Firstly, the TPE-IMB model achieves a significant improvement, reducing MAE from 34.86 MW in the original boosting model to 30.38 MW, and RMSE from 47.79 MW to 40.45 MW. Secondly, the hybrid TPE-IMB-CS-BiLSTM model further enhances accuracy, reducing MAE to 27.77 MW and RMSE to 36.55 MW, significantly outperforming the TPE-IMB and vanilla BiLSTM models. Lastly, compared to other state-of-the-art models, the proposed model achieves the best performance with a 24.66 % reduction in MAE and 26.74 % reduction in RMSE, demonstrating superior robustness in handling complex and extreme data conditions.

Keywords: STLF; Machine learning; Time-series forecasting; InMetra Boost (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:328:y:2025:i:c:s0360544225022248

DOI: 10.1016/j.energy.2025.136582

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