Advanced method for short‐term wind power prediction with multiple observation points using extreme learning machines. Issue 1 (15th December 2017)

• Record Type: Journal Article
• Authors: Mahmoud, Tawfek; Dong, Zhao Yang; Ma, Jin
• Is Part Of: Journal of engineering. Volume 2018:Issue 1(2018)
• Page Start: 29
• Page End: 38
• Publication Date: 2017-12-15
• Subjects: power engineering computing -- grey systems -- mean square error methods -- support vector machines -- fuzzy reasoning -- fuzzy neural nets -- time series -- learning (artificial intelligence) -- wind power plants; mean absolute error -- Grey predictor model -- wind power estimation -- root mean square error -- short‐term wind power prediction -- artificial intelligence technique -- high‐quality wind power prediction -- power system planning -- power system operation -- power system control -- advanced time series processing method -- extreme learning machine technique -- historical records -- numerical weather information -- real wind farm sites -- Australia region -- wind speed -- capacity factor -- wind power‐speed curve -- artificial neural network -- fuzzy logic model -- adaptive neuro‐fuzzy inference system -- support vector machine model -- model verification -- real wind measurements -- weather station -- Australia; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2017.0338
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.