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

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.