Optimal Granule-Based PIs Construction for Solar Irradiance Forecast

Songjian Chai, Zhao Xu, Wai Kin Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)


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.
Original languageEnglish
Article number7268775
Pages (from-to)3332-3333
Number of pages2
JournalIEEE Transactions on Power Systems
Issue number4
Publication statusPublished - 1 Jul 2016


  • 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


Dive into the research topics of 'Optimal Granule-Based PIs Construction for Solar Irradiance Forecast'. Together they form a unique fingerprint.

Cite this