Advanced method for short-term wind power prediction with multiple observation points using extreme learning machines

The Journal of Engineering

Zhao Yang Dong¹  Tawfek Mahmoud² 

[1] School of Electrical Engineering and Telecommunications, The University of NSW , Sydney , Australia;School of Electrical and Information Engineering, The University of Sydney , NSW 2006 , Sydney , Australia

关键词: Australia region;    real wind farm sites;    historical records;    short-term wind power prediction;    high-quality wind power prediction;    artificial neural network;    fuzzy logic model;    mean absolute error;    capacity factor;    Australia;    numerical weather information;    model verification;    power system control;    real wind measurements;    wind speed;    extreme learning machine technique;    power system operation;    wind power estimation;    power system planning;    root mean square error;    advanced time series processing method;    Grey predictor model;    weather station;    adaptive neuro-fuzzy inference system;    artificial intelligence technique;    support vector machine model;    wind power-speed curve;   

DOI  :  10.1049/joe.2017.0338

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

This research paper presents an advanced approach to enhance the short-term wind power prediction based on artificial intelligence techniques. A high-quality wind power prediction is essential for power system planning, operation, and control. Thus, a new novel approach has been developed to improve the quality and reliability of the calculated results by integrating advanced time series processing method and the extreme learning machine technique. Moreover, historical records are utilised from numerical weather information and multiple observations points close to real wind farm sites within Australia regions. The wind speed is assessed by using the developed model in the first stage, and then the wind power and capacity factor is calculated using wind power–speed curve for each observation site. Artificial neural network, fuzzy logic (adaptive neuro-fuzzy inference system), and support vector machine models are used for model verifications, validations, and practical applications. The developed model is tested using real wind measurements by Bureau of Meteorology, 15 selected weather stations corresponded to the locations of nearby real wind farm sites in Australia. The demonstrated results and performance indicators, e.g. root mean square error and mean absolute error are compared with Khalid, persistence, and Grey predictor models for validations and verifications reasons. As the potential gains over other techniques, the proposed model has found more efficient and superior for wind power estimation and prediction than other developed conventional methods and models, which in turn improves the power system performance, and reduces the economic impacts.

【 授权许可】

CC BY