Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model

Yanhui Liu, Jiulong Wang (), Lingyun Song, Yicheng Liu and Liqun Shen
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Yanhui Liu: Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China
Jiulong Wang: Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China
Lingyun Song: Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China
Yicheng Liu: Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China
Liqun Shen: School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Energies, 2025, vol. 18, issue 13, 1-22

Abstract: Accurate short-term photovoltaic (PV) power forecasting is crucial for ensuring the stability and efficiency of modern power systems, particularly given the intermittent and nonlinear characteristics of solar energy. This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. First, the original PV power signal was decomposed into intrinsic mode functions using a modified CEEMDAN method to better capture the complex nonlinear features. Subsequently, the fast Fourier transform and improved Pearson correlation coefficient (IPCC) were applied to identify and merge similar-frequency intrinsic mode functions, forming new composite components. Each reconstructed component was then forecasted individually using a BiLSTM model, whose parameters were optimized by the JS algorithm. Finally, the predicted components were aggregated to generate the final forecast output. Experimental results on real-world PV datasets demonstrate that the proposed CEEMDAN-JS-BiLSTM model achieves an R 2 of 0.9785, a MAPE of 8.1231%, and an RMSE of 37.2833, outperforming several commonly used forecasting models by a substantial margin in prediction accuracy. This highlights its effectiveness as a promising solution for intelligent PV power management.

Keywords: PV power; CEEMDAN; jellyfish search algorithm; BiLSTM; short-term PV forecasting (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: 2025
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