Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network

Ze Wu, Feifan Pan, Dandan Li, Hao He, Tiancheng Zhang and Shuyun Yang ()
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Ze Wu: College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China
Feifan Pan: College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China
Dandan Li: College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China
Hao He: College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China
Tiancheng Zhang: College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China
Shuyun Yang: College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China

Sustainability, 2022, vol. 14, issue 20, 1-16

Abstract: Accurate prediction of photovoltaic power is of great significance to the safe operation of power grids. In order to improve the prediction accuracy, a similar day clustering convolutional neural network (CNN)–informer model was proposed to predict the photovoltaic power. Based on correlation analysis, it was determined that global horizontal radiation was the meteorological factor that had the greatest impact on photovoltaic power, and the dataset was divided into four categories according to the correlation between meteorological factors and photovoltaic power fluctuation characteristics; then, a CNN was used to extract the feature information and trends of different subsets, and the features output by CNN were fused and input into the informer model. The informer model was used to establish the temporal feature relationship between historical data, and the final photovoltaic power generation power prediction result was obtained. The experimental results show that the proposed CNN–informer prediction method has high accuracy and stability in photovoltaic power generation prediction and outperforms other deep learning methods.

Keywords: photovoltaic power prediction; machine learning; CNN; CNN–informer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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