Improved super-resolution perception convolutional neural network for photovoltaics missing data recovery

Errors in data measurement and recording can induce data missing for photovoltaic (PV) operation. Flawed data, however, can greatly impair the performance of data-driven models such as the accuracy of PV generation forecasting. The superresolution perception convolutional neural network (SRPCNN) is a powerful tool in data reconstruction from low frequency to high frequency and it can be used for data recovery. The SRPCNN, however, has problems in dealing with null values for PV data recovery. To solve this problem, an improved SRPCNN model, which integrates naive SPRCNN and linear interpolation, is proposed in this paper. Experiments are carried out under 5 different missing rates. The improved model can handle null values better, and can have a better accuracy improvement than the naive SPRCNN and other methods. To further improve the data recovery performance, a modified whale optimization algorithm is deployed to optimize the hyperparameters of SPRCNN. To verify the efficiency of the proposed model, extensive numerical experiments on different types of data from an actual PV power plant have been conducted by comparing with benchmarks. Compared with the naive SRPCNN, the RMSE and MAE of the proposed model are reduced by more than 30% in various cases. Results show that the improved SRPCNN model is efficient in PV data recovery with various missing rates. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper proposes an improved SRPCNN model for PV data recovery, which can better handle data missing issues and achieve better accuracy improvement. Furthermore, the use of a modified whale optimization algorithm to optimize the hyperparameters of the model further enhances the data recovery performance.