Research on Pearson correlation and improved CNN-LSTM algorithm for predicting photovoltaic power generation

Jiayuan Ma; Ying Cuan

doi:10.1117/12.3030182

5 June 2024 Research on Pearson correlation and improved CNN-LSTM algorithm for predicting photovoltaic power generation

Jiayuan Ma, Ying Cuan

Author Affiliations +

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 1316329 (2024) https://doi.org/10.1117/12.3030182
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

To enhance the grid connection reliability of distributed photovoltaic power plants, a hybrid prediction model integrating a convolutional neural network and short-term memory is proposed. Spatial features are extracted via CNN, while LSTM captures temporal dependencies in power generation data. Initially, leveraging clustering and partitioning of weather data, the Pearson correlation coefficient method is employed to analyze correlations between meteorological factors (e.g., solar radiation, temperature, relative humidity) and photovoltaic power generation. Subsequently, the Sparrow Search Algorithm is applied to optimize the prediction model. Experimental findings using photovoltaic power generation data in Dingbian County reveal that the Sparrow Optimization Algorithm significantly enhances prediction accuracy and improves the scheduling stability of distributed photovoltaic power stations.

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

Citation Download Citation

Jiayuan Ma and Ying Cuan "Research on Pearson correlation and improved CNN-LSTM algorithm for predicting photovoltaic power generation", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 1316329 (5 June 2024); https://doi.org/10.1117/12.3030182

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