Solar power prediction with data source weighted nearest neighbors

Abstract

We consider the task of predicting the half-hourly solar PhotoVoltaic (PV) power output for the next day from three sources: previous solar power, previous weather data and weather forecasts. We propose DWkNN, a data source weighed nearest neighbor method that considers the importance of the different data sources and learns the best weights for them. We evaluate its performance using Australian PV and weather data for one year. Our results show that DWkNN is more accurate than the standard nearest neighbor and other machine learning, statistical and persistence methods used for comparison. We assess the importance of the three data sources to predict the PV power output and show that the most useful information source is the weather forecast, followed by the previous weather and previous PV power data. The addition of the daily average solar irradiance forecast for the next day, when this information is available, considerably improves the accuracy.