Photovoltaic power prediction based on optimized BP neural network

Jun Yang; Na Liu

doi:10.1117/12.3032918

5 July 2024 Photovoltaic power prediction based on optimized BP neural network

Jun Yang, Na Liu

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131845W (2024) https://doi.org/10.1117/12.3032918
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

With advancements in social science and technology, along with the widespread implementation of photovoltaic power generation in the energy sector, accurately predicting photovoltaic power output has emerged as a crucial challenge for enhancing energy management efficiency. To address this, the BP neural network architecture is employed to enhance the precision of power output prediction. Key factors influencing enhancing photovoltaic power prediction accuracy., obtained from both historical data and weather forecasts, are incorporated. Experimental results demonstrate the optimization proposed in this study improving photovoltaic power prediction accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Yang and Na Liu "Photovoltaic power prediction based on optimized BP neural network", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131845W (5 July 2024); https://doi.org/10.1117/12.3032918

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