PV prediction model based on XGBoost with ISMA optimization

Fuyou Mao; Yuang Jiang; Boran Cao; Dianmo Wu

doi:10.1117/12.2684554

19 October 2023 PV prediction model based on XGBoost with ISMA optimization

Fuyou Mao, Yuang Jiang, Boran Cao, Dianmo Wu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127091S (2023) https://doi.org/10.1117/12.2684554
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Accurate Photovoltaic power (PV) power forecasting is the basis and key to grid dispatch management. With machine learning algorithms and the latest swarm intelligence algorithms being proposed, a reasonable combination of the two will produce good prediction results. This paper addresses the problem of optimal selection of hyperparameters for the XGBoost algorithm in the PV power prediction problem. This paper establishes an XGBoost long-term PV power prediction model based on the optimization of ISMA algorithm, firstly, the dataset is pre-processed and the training set and test set are divided, then the data is trained, the model with the best practical optimization results is predicted, and finally the predicted results of the model are compared with the classical models. The experimental results show that the optimized XGBoost PV power prediction model based on ISMA algorithm can achieve better PV prediction results with MAE of 0.154, MSE of 0.256 and R² of 0.964, which are better than other models.

Citation Download Citation

Fuyou Mao, Yuang Jiang, Boran Cao, and Dianmo Wu "PV prediction model based on XGBoost with ISMA optimization", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127091S (19 October 2023); https://doi.org/10.1117/12.2684554

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