Clustering based methods for solar power forecasting

Abstract

Accurate forecasting of solar power is needed for the successful integration of solar energy into the electricity grid. In this paper we consider the task of predicting the half-hourly solar photovoltaic power for the next day from previous solar power and weather data. We propose and evaluate several clustering based methods, that group the days based on the weather characteristics and then build a separate prediction model for each cluster using the solar power data. We compare these methods with their non-clustering based counterparts, and also with non-clustering based methods that build a single prediction model for all types of days. We conduct a comprehensive evaluation using Australian data for two years. Our results show that the most accurate prediction model was the clustering based nearest neighbor which uses a vector of half-hourly solar irradiance for the clustering. It achieved MAE=59.81 KW, outperforming all other clustering and non-clustering based methods and baselines.