HIFA: Promising Heterogeneous Solar Irradiance Forecasting Approach Based on Kernel Mapping

The rapid employment of photovoltaic (PV) has highlighted the importance of accurate solar irradiance forecasting in grid operation. However, the intermittent nature of solar irradiance represents a big challenge and degrades the accuracy of forecasting techniques, posing towards developing ensemble-based approaches. Most ensemble approaches generate weights based on the performance of individual forecasting models (IFMs) where linear operations are often used to aggregate them. The generalization of such weights could not be practically guaranteed due to the high variability among predictions obtained by IFMs. To tackle these issues, a novel heterogeneous solar irradiance forecasting approach, so-called HIFA, is proposed in this article. Specifically, we propose an effective aggregation strategy based on kernel mapping for aggregating the predictions of accurate deep learning based IFMs. The proposed aggregation strategy can properly map the predictions of IFMs onto a consensus prediction. HIFA utilizes efficient deep recurrent neural networks, which can exploit long-term information from previous computations to model the fluctuated solar irradiance, for building the IFMs. The results reveal that HIFA substantially improves the accuracy of solar irradiance forecasting when compared to ensemble-based approaches, thanks to the generalization capability of the proposed aggregation strategy and the high accuracy of deep IFMs.