Short-term photovoltaic power forecasting method considering historical information reuse and numerical weather forecasting

We proposed a novel historical feature reuse scheme to improve the accuracy of photovoltaic power forecasting model. Firstly, a weather type classification method based on the Elkan K-means algorithm was proposed, and a feature matching mechanism based on Markov distance was constructed to fuse information; Then, a bidirectional recurrent residual network was constructed, which improved the feature extraction performance of the forecasting model for different photovoltaic output scenarios; Finally, an error decoupling mechanism was proposed to evaluate the upper limit of the forecasting accuracy of the model. Taking the data provided by a photovoltaic power station in Jilin Province, China as the research object, the day-ahead power forecasting accuracy is 91.12 %, verifying the validity of the proposed model.