Prediction Interval Estimation and Deterministic Forecasting Model Using Ground-Based Sky Image

Due to the explosive penetration of photovoltaic (PV) systems in the power grid, accurate and well-informed solar PV power forecasting has become exceptionally crucial, while accurate irradiance prediction is the basis of PV power forecasting. Yet most research on PV power/irradiance forecasting focus on deterministic forecasting, whereas it is difficult to express the credibility of the forecasting results, especially in the scene of instantaneous fluctuation of irradiance and photovoltaic power caused by cloud movement. Therefore, in order to accurately estimate and track the rapid fluctuations of solar irradiance, this paper takes the ground-based sky image information into consideration and obtains the probability-level prediction results along with the deterministic point forecasting, proposes a comprehensive evaluation function constructed with the average coverage and the average width as the fitness function to assist in the optimization of the deterministic forecasting results, and then constructs a three-output model, naming it DenLUBE. In addition, a framework for irradiance variation pattern prediction is proposed to provide pattern information for the forecasting time period to further improve the performance of the ultra-short-term forecasting model. The results show that the proposed model can achieve high quality and strongly robust deterministic forecasting results while producing tight prediction intervals independent of weather conditions. In addition, the performance of our model is greatly enhanced compared to the traditional approach of taking the middle position of the interval as the deterministic forecasting result, as well as other benchmark models.