Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

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

doi:10.1109/TPWRS.2016.2608740

Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

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

Tsinghua University
Hong Kong Polytechnic University

Research output: Contribution to journal › Journal article › Research › peer-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 language	English
Journal	IEEE Transactions on Power Systems
Volume	32
Issue number	3
Pages (from-to)	2471 - 2472
ISSN	0885-8950
DOIs	https://doi.org/10.1109/TPWRS.2016.2608740
Publication status	Published - 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

PV power
Forecasting
Prediction intervals
Extreme learning machine
Quantile regression

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10.1109/TPWRS.2016.2608740

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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. ",

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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

SN - 0885-8950

VL - 32

SP - 2471

EP - 2472

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

IS - 3

ER -

Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

Abstract

UN SDGs

Keywords

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