Title :
Optimal Prediction Intervals of Wind Power Generation
Author :
Can Wan ; Zhao Xu ; Pinson, Pierre ; Zhao Yang Dong ; Kit Po Wong
Author_Institution :
Dept. of Electr. Eng., Hong Kong Polytech. Univ., Hong Kong, China
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.
Keywords :
inference mechanisms; learning (artificial intelligence); load forecasting; particle swarm optimisation; power generation reliability; probability; statistical analysis; wind power plants; Australia; distribution assumption; extreme learning machine; hybrid intelligent algorithm approach; optimal prediction interval; particle swarm optimization; power system operation; probabilistic interval forecasting; reliability; statistical inference; wind farm data; wind power forecasting; wind power generation; Forecasting; Linear programming; Power system reliability; Reliability; Training; Wind forecasting; Wind power generation; Extreme learning machine; forecasts; particle swarm optimization; prediction intervals; wind power;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2013.2288100