Short term photovoltaic power generation prediction model based on improved GA-LSTM neural network

Fang Qiu; Ning Wang; Yue Wang

doi:10.1117/12.2640200

1 August 2022 Short term photovoltaic power generation prediction model based on improved GA-LSTM neural network

Fang Qiu, Ning Wang, Yue Wang

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 1225704 (2022) https://doi.org/10.1117/12.2640200
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

In this paper, genetic algorithm is used to improve LSTM neural network, and then a photovoltaic power prediction model is obtained. After preprocessing and normalizing the original power generation, The LSTM considering both time and nonlinearity is used for training, and finally it is improved by genetic algorithm. The optimized LSTM model is applied to the problems in short-term prediction of photovoltaic power generation, and compared with traditional methods, so as to achieve more accurate prediction results.

Citation Download Citation

Fang Qiu, Ning Wang, and Yue Wang "Short term photovoltaic power generation prediction model based on improved GA-LSTM neural network", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 1225704 (1 August 2022); https://doi.org/10.1117/12.2640200

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