Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning

Abstract

We consider the task of simultaneously predicting the solar power output for the next day at half-hourly intervals using data from three related time series: solar, weather and weather forecast. We propose PSF3, a novel pattern sequence forecasting approach, an extension of the standard PSF algorithm, which uses all three time series for clustering, pattern sequence extraction and matching. We evaluate its performance on two Australian datasets from different climate zones; the results show that PSF3 is more accurate than the other PSF methods. We also investigate if a dynamic meta-learning ensemble combining the two best methods, PSF3 and a neural network, can further improve the results. We propose a new weighting strategy for combining the predictions of the ensemble members and compare it with other strategies. The overall most accurate prediction model is the meta-learning ensemble with the proposed weighting strategy.