Short-term prediction of photovoltaic power generation based on sand cat group optimization

Yupeng Qiu; Yixin Su

doi:10.1117/12.3032876

5 July 2024 Short-term prediction of photovoltaic power generation based on sand cat group optimization

Yupeng Qiu, Yixin Su

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131840I (2024) https://doi.org/10.1117/12.3032876
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The academic paper introduces a multi-strategy enhanced Sand Cat Swarm Algorithm (TL-SCSO) designed to tackle the challenges of limited population diversity, poor convergence accuracy, and susceptibility to local optima. The algorithm leverages Cubic chaos mapping to enrich the initial population distribution, ensuring a more diverse and representative starting point. Additionally, an adaptive t-distribution perturbation approach is employed to maintain population diversity throughout the optimization process. The incorporation of the golden ratio into the Levi's flight strategy enables the algorithm to effectively navigate away from local optima, enhancing its overall performance. Finally, the algorithm is utilized to optimize the hyperparameters of BiTCN-BiLSTM-Attention, thereby improving model training speed and prediction accuracy. The algorithm's efficacy is demonstrated through its application to data from a photovoltaic power station in central China, showcasing its superior stability and convergence accuracy under various weather conditions.

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

Citation Download Citation

Yupeng Qiu and Yixin Su "Short-term prediction of photovoltaic power generation based on sand cat group optimization", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131840I (5 July 2024); https://doi.org/10.1117/12.3032876

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