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2025-12-28
Short-Term Photovoltaic Power Prediction Based on SCC-CEEMDAN-HO-BiLSTM
By
Progress In Electromagnetics Research C, Vol. 164, 58-68, 2026
Abstract
To address the challenge of high prediction difficulty caused by the random volatility of photovoltaic (PV) power output, this paper proposes a hybrid forecasting model that deeply integrates multi-scale feature analysis with an intelligent optimization algorithm. First, the spearman correlation coefficient (SCC) is used to select influencing factors as model inputs, and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to extract multi-scale features from the power data across four seasons. Second, the hippopotamus optimization (HO) algorithm is introduced in order to overcome the randomness and inefficiency of manual hyperparameter tuning and to optimize the hyperparameters of the bidirectional long short-term memory (BiLSTM) network. Through multi-seasonal case studies, the pro-posed SCC-CEEMDAN-HO-BiLSTM model outperforms conventional models. Specifically, it shows significant improvements in both prediction accuracy and robustness compared to benchmark methods such as the standalone BiLSTM model and the unoptimized CEEMDAN-BiLSTM model. The model effectively handles the multi-scale fluctuations in PV power sequences and meets the requirements for short-term photovoltaic power forecasting.
Citation
Jianwei Liang, Jie Yue, Yanli Xin, Shuxin Pan, Jiaming Tian, and Jingxuan Sun, "Short-Term Photovoltaic Power Prediction Based on SCC-CEEMDAN-HO-BiLSTM," Progress In Electromagnetics Research C, Vol. 164, 58-68, 2026.
doi:10.2528/PIERC25101401
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