Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

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

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

This letter proposes a novel efficient probabilistic forecasting approach 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 proposed based on 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
JournalI E E E Transactions on Power Systems
Volume32
Issue number3
Pages (from-to)2471 - 2472
ISSN0885-8950
DOIs
Publication statusPublished - 2017

Keywords

  • PV power
  • Forecasting
  • Prediction intervals
  • Extreme learning machine
  • Quantile regression

Cite this

Wan, Can ; Lin, Jin ; Song, Yonghua ; Xu, Zhao ; Yang, Guangya. / Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach. In: I E E E Transactions on Power Systems. 2017 ; Vol. 32, No. 3. pp. 2471 - 2472.
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Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach. / Wan, Can; Lin, Jin; Song, Yonghua; Xu, Zhao; Yang, Guangya.

In: I E E E Transactions on Power Systems, Vol. 32, No. 3, 2017, p. 2471 - 2472.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

AU - Wan, Can

AU - Lin, Jin

AU - Song, Yonghua

AU - Xu, Zhao

AU - Yang, Guangya

PY - 2017

Y1 - 2017

N2 - This letter proposes a novel efficient probabilistic forecasting approach 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 proposed based on 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.

AB - This letter proposes a novel efficient probabilistic forecasting approach 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 proposed based on 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.

KW - PV power

KW - Forecasting

KW - Prediction intervals

KW - Extreme learning machine

KW - Quantile regression

U2 - 10.1109/TPWRS.2016.2608740

DO - 10.1109/TPWRS.2016.2608740

M3 - Journal article

VL - 32

SP - 2471

EP - 2472

JO - I E E E Transactions on Power Systems

JF - I E E E Transactions on Power Systems

SN - 0885-8950

IS - 3

ER -