A Hybrid Decomposition and Deep Learning Model for Photovoltaic Power Forecasting Under Variable Meteorological Conditions

Liusong Huang, Adam Amril bin Jaharadak, Nor Izzati Ahmad and Jie Wang
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Liusong Huang: Management and Science University, Malaysia & Maanshan Teacher's College, China
Adam Amril bin Jaharadak: Management and Science University, Malaysia
Nor Izzati Ahmad: Management and Science University, Malaysia
Jie Wang: Maanshan Teacher's College, China

International Journal of Data Warehousing and Mining (IJDWM), 2025, vol. 21, issue 1, 1-22

Abstract: To improve photovoltaic (PV) power forecasting under variable meteorological conditions, this paper proposes a hybrid model combining signal decomposition, clustering, and deep learning. An improved complete ensemble empirical mode decomposition with adaptive noise method is used for multi-scale decomposition of meteorological inputs such as temperature, solar radiation, and wind direction. Sample entropy-guided K-means clustering segments signals into high, medium, and low-frequency components, with high-frequency parts further denoised using variational mode decomposition. A convolutional neural network-bidirectional long short-term memory network is then optimized by the crown porcupine optimization algorithm to fine-tune key hyperparameters. Experiments on real PV data show a 20% root mean squared error reduction (to 7.30 kW), demonstrating strong adaptability and robustness for intelligent PV scheduling.

Date: 2025
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