LAUSR

For better deep learning forecasting systems for photovoltaic systems, confidence information about a point forecast is necessary in practical cases where uncertainties are unavoidable. In this study, using Bayesian deep learning, the authors introduce a confidence-aware deep learning forecasting system that provides confidence information as well as a point forecast. Through the experiments using the real-world data, they first solve three main issues caused by when Bayesian deep learning is applied to the forecasting of daily solar irradiance using weather forecast: selection of neural network model, selection of validation data to be used for estimating the confidence information, and ways for estimating the confidence information. Then, they examine the feasibility of the confidence-aware deep learning forecasting system in estimating the confidence information. From the experiments, classifying the forecast outputs into confident outputs and non-confident outputs using the confidence information, they show that maximum absolute percentage error of confident forecast outputs and non-confident forecast outputs are 5 and 22.8% at a specific classification threshold, respectively. This result shows that their confidence-aware deep learning forecasting system is good to estimate meaningful confidence information that is closely related to the forecast accuracy.