Distributed Photovoltaic Power Forecasting Based on Auto Machine Learning Framework and Fusion Perception Technology

Distributed photovoltaic power forecasting plays a vital role in renewable energy management. This study is dedicated to developing a distributed photovoltaic power forecasting method based on automatic machine learning (AutoML) framework and fusion perception technology. This method constructs a highly accurate and adaptable distributed photovoltaic power station power forecasting model through automatic selection of data features, automatic clustering analysis of weather categories, and deep learning method based on ResNet. Experimental results show that the model performs well under different weather conditions, and the monthly average accuracy of short-term forecasts reaches 95.05%.