Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

Can Wan, Jin Lin, Yonghua Song, Zhao Xu, Guangya Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

95 Citations (Scopus)

Abstract

A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming-based prediction interval construction model for PV power generation is established based on an extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.
Original languageEnglish
Pages (from-to)2471-2472
Number of pages2
JournalIEEE Transactions on Power Systems
Volume32
Issue number3
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • Extreme learning machine
  • prediction intervals
  • probabilistic forecasting
  • PV power
  • quantile regression

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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