New Energy Power Prediction and Warning Based on Multi-source Prediction and Scene Classification Recognition

Abstract

This article proposes a new method for predicting and warning of new energy electricity based on a multi-source prediction data and scene classification recognition algorithm. The study uses ECMWF, GFS, and DERF2.0 multi-source prediction data to improve the accuracy of offshore wind power prediction and disaster warning. In order to overcome the limitations of traditional prediction methods, this study adopts a scene classification recognition method. Based on meteorological, power grid, and environmental data, the study uses a scene recognition algorithm to establish three types of factor feature extraction models and combines them with fault information to establish a wind power prediction and warning model. This model can achieve the prediction of wind power and warn of possible power grid faults under meteorological disasters. Case analysis shows that this method significantly improves the accuracy of new energy electricity prediction and meteorological disaster warning. This study emphasizes the importance of multi-source prediction data and scene classification recognition methods in improving offshore wind power prediction and other new energy fields. Additionally, this method has the potential to make valuable contributions to the renewable energy industry.