This letter proposes a novel granule computing-based framework for prediction intervals (PIs) construction of solar irradiance time series that has significant impacts on solar power production. Distinguished from most existing methods, the new framework can address both stochastic and knowledge uncertainties in constructing PIs. The proposed method has proved to be highly effective in terms of both reliability and sharpness through a real case study using measurement data obtained from Hong Kong Observatory.
- Granular neural network
- prediction intervals
- random vector forward link (RVFL)
- solar irradiance forecasting
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering