Affiliations: [a] BEARS, University Town, NUS Campus, Singapore
| [b] Department of Electrical Engineering Yeungnam University Gyeongson, South Korea | [c] Deparment of Electrical Engineering, College of Engineering, Majmaah University, Riyadh, Saudi Arabia
| [d] Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia
| [e] Saudi Electricity Company Chair in Power System Reliability and Security, King Saud University, Riyadh, Saudi Arabia
Correspondence:
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Corresponding author. Tahir Khurshaid, Department of Electrical Engineering Yeungnam University Gyeongson, 38541, South Korea. E-mail: [email protected].
Abstract: In this paper, an intelligent approach for short-term wind speed forecasting (STWSF) is proposed. The STWSF models are developed to forecast the wind speed into a multi-step ahead forecasting, which is used to demonstrate the daily forecast results in One-Step-Ahead (OSA), Two-Step-Ahead (TSA), and Three-Step-Ahead (ThSA) based forecasting manner. To demonstrate the performance and results of the proposed approach, the real-site logged dataset is used for training and testing phase of the year 2015 to 2017. The STWSF is achieved recursively by utilizing the forecasted data in step-1 (OSA) as an input to generate the next forecasting data (in step-2 TSA) and the process is achieved upto level of step-3 (ThSA) forecasting. In order to results demonstration of fair adoptability of the proposed approach, different neural networks (NNs) models are developed for the same dataset, which shows that the proposed STWSF approach is outperformed and can be utilized for other locations for future applications.
