Short-Term Photovoltaic Power Prediction Based on IWOA-TCN-BiGRU-MATT

Guanglin Sha¹, Xinwei Cong¹, Yunzhao Wu¹, Dinghong Chen¹, Bo Wang², Hengrui Ma^2,*
1 China Electric Power Research Institute Co., Ltd., Beijing, China
2 School of Electrical and Automation, Wuhan University, Wuhan, China
* Corresponding Author: Hengrui Ma. Email: email
(This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.081823

Received 09 March 2026; Accepted 27 May 2026; Published online 17 September 2026

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Abstract

To address the challenges of significant nonlinearity, intricate temporal interdependencies, and the tendency to get stuck in local optima during parameter tuning in short-term photovoltaic (photovoltaic, PV) power forecasting, this paper puts forward a hybrid model called IWOA-TCN-BiGRU-MATT. This model fuses the IWOA (Improved Whale Optimization Algorithm, IWOA) with the TCN (Temporal Convolutional Network, TCN), BiGRU (Bidirectional Gated Recurrent Unit, BiGRU), and MATT. By leveraging IWOA to boost parameter optimization efficiency and integrating TCN’s ability to extract multi-scale features, BiGRU’s bidirectional temporal modeling, and MATT’s emphasis on key features, the model aims to realize high-precision PV power prediction. Based on measured data from a PV power station in Central China, experimental findings show that the proposed model surpasses competing methods under various meteorological scenarios, demonstrating improved prediction accuracy under different weather conditions.

Keywords

Improved whale optimization algorithm; temporal convolutional network; bidirectional gated recurrent unit; multi-head attention mechanism; photovoltaic power prediction; intelligent optimization

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Short-Term Photovoltaic Power Prediction Based on IWOA-TCN-BiGRU-MATT

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