Photovoltaic Power Generation Prediction Based on AWOA-BI-LSTM

Journal Title: Journal of Shenyang Agricultural University - Year 2025, Vol 56, Issue 2

Abstract

[Objective]Accurate prediction of photovoltaic (PV) power generation is crucial for integrating renewable energy into the grid, energy markets, and building energy management systems. To improve prediction accuracy, this study proposed a hybrid model, denoted as AWOA-Bi-LSTM, which combines an improved Whale Optimization Algorithm (AWOA) with a Bidirectional Long ShortTerm Memory network (Bi-LSTM). To solve the problems of low optimization accuracy and slow convergence of the traditional Whale Optimization Algorithm (WOA), two improvement strategies, dynamic weight factors and adaptive parameter adjustment, are introduced to enhance the model’s global search capability and convergence efficiency. [Methods] Based on the real PV power generation data and measured meteorological data, comparative experiments were conducted among AWOA-Bi-LSTM, WOA-Bi-LSTM, and GRNN. [Results]The R² values of AWOA-Bi-LSTM model on the test set and training set are 0.997 01 and 0.998 43, respectively; the RMSE values are 1.585 and 0.900 63, respectively; and the RPD are 20.160 4 for the test set and 25.935 7 for the training set. [Conclusion]The results demonstrate that the AWOA-Bi-LSTM outperforms conventional methods in terms of goodness of fit, prediction accuracy, and stability. It more effectively captures complex patterns and trends in time series data, substantially enhancing prediction performance.

Authors and Affiliations

WU Shihong, ZHANG Bichen, WU Jiawen, WU Xingyu

Keywords

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  • EP ID EP766973
  • DOI 10.3969/j.issn.1000-1700.2025.02.014
  • Views 13
  • Downloads 0

How To Cite

WU Shihong, ZHANG Bichen, WU Jiawen, WU Xingyu (2025). Photovoltaic Power Generation Prediction Based on AWOA-BI-LSTM. Journal of Shenyang Agricultural University, 56(2), -. https://www.europub.co.uk/articles/-A-766973