Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Short-term wind power prediction based on extreme learning machine with error correction

Li, Zhi, Ye, Lin, Zhao, Yongning, Song, Xuri, Teng, Jingzhu and Jin, Jingxin 2016. Short-term wind power prediction based on extreme learning machine with error correction. Protection and Control of Modern Power Systems 1 , 1. 10.1186/s41601-016-0016-y

[thumbnail of s41601-016-0016-y.pdf]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

Introduction: Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind. The fluctuation of the wind generation has a great impact on the unit commitment. Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way. Methods: In this paper, a combined approach based on Extreme Learning Machine (ELM) and an error correction model is proposed to predict wind power in the short-term time scale. Firstly an ELM is utilized to forecast the short-term wind power. Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method. Results: For short-term forecasting, the Extreme Learning Machine (ELM) doesn’t perform well. The overall NRMSE (Normalized Root Mean Square Error) of forecasting results for 66 days is 21.09 %. For the ultra-short term forecasting after error correction, most of forecasting errors lie in the interval of [−10 MW, 10 MW]. The error distribution is concentrated and almost unbiased. The overall NRMSE is 5.76 %. Conclusion: The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error (NRMSE).

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Springer
ISSN: 2367-2617
Date of First Compliant Deposit: 9 August 2019
Date of Acceptance: 10 May 2016
Last Modified: 20 Oct 2021 01:21
URI: https://orca.cardiff.ac.uk/id/eprint/124806

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics