Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence

Songjian Chai, Ming Niu, Zhao Xu, Loi Lei Lai, Kit Po Wong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

The high penetration of solar PV generations brings about significant challenges for decision-makers of power system operation due to high volatility and uncertainty it involves. In recent years, it has been demonstrated by many researchers that the probabilistic interval forecast could significantly facilitate some decision-making cases, such as storage optimization, market bidding, reserves setting, as it can provide the uncertainty information associated with the point estimations. This paper proposes a nonparametric conditional interval forecast method for PV power generation which can capture the interdependence among the real power output and their point forecasts within all forecasting horizons of interests. The proposed model is tested using the dataset of PV generation power measurements and day-ahead point forecasts in Belgium. The results based on reliability and interval score performance metrics illustrate the effectiveness of the proposed model.
Original languageEnglish
Title of host publication2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PublisherIEEE Computer Society
Volume2016-November
ISBN (Electronic)9781509041688
DOIs
Publication statusPublished - 10 Nov 2016
Event2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States
Duration: 17 Jul 201621 Jul 2016

Conference

Conference2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Country/TerritoryUnited States
CityBoston
Period17/07/1621/07/16

Keywords

  • Conditional forecast
  • Copula
  • Kernel density estimation
  • PV power forecast
  • Temporal dependence

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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