Optimal prediction intervals of wind power generation

Can Wan, Zhao Wu, Pierre Pinson, Zhao Yang Dong, Kit Po Wong

Research output: Contribution to journalJournal articlepeer-review

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Abstract

Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with Associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems.
Original languageEnglish
JournalIEEE Transactions on Power Systems
Volume29
Issue number3
Number of pages9
ISSN0885-8950
DOIs
Publication statusPublished - 2014

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