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

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

173 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 language	English
Pages (from-to)	2471-2472
Number of pages	2
Journal	IEEE Transactions on Power Systems
Volume	32
Issue number	3
DOIs	https://doi.org/10.1109/TPWRS.2016.2608740
Publication status	Published - 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

UN SDGs

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

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10.1109/TPWRS.2016.2608740

Cite this

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KW - prediction intervals

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KW - quantile regression

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Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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