Ensemble Machine Learning Method for Photovoltaic Power Forecasting

Abstract

Photovoltaic power prediction plays an extremely important role in the construction of smart power grid and power grid security protection. In order to solve the problem of unstable power generation and even damages to the power grid caused by the ever changing irradiance and meteorological conditions, this paper leverages the traditional time series prediction modeling method to the machine learning approaches, such as recurrent neural network (RNN), convolutional neural network (CNN) and decision tree (DT), and uses the ensemble machine learning method to improve the final prediction accuracy, by training and testing on the radiation and meteorological data collected from a photovoltaic power station in Gansu Province of China, which enjoys the best solar resources in the country. The experimental results show that the ensemble model achieves the highest prediction accuracy, and its root mean square error(RMSE) is 0.4477. This is of great significance to the power generation evaluation and dispatching of photovoltaic power station.