Short Term Wind Speed Forecasting using PSO Optimized Regression Model
A.M.S.V Sushma1, D.J.V Prasad2
1A.M.S.V. Sushm, Department of Electrical & Electronics Engineering, SRKR Engineering Collge, Bhimavaram (Andhra Pradesh), India.
2D.J.V. Prasad, Department of Electrical & Electronics Engineering, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
Manuscript received on 13 May 2019 | Revised Manuscript received on 07 June 2019 | Manuscript Published on 15 June 2019 | PP: 237-242 | Volume-8 Issue-1S3 June 2019 | Retrieval Number: A10420681S319/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main research results of wind speed prediction research. It only focuses on point prediction. Wind power depends on atmospheric variables that are always changing. Wind speed prediction is an important aspect of future safe operation and reliability of the power grid. Outline the new developments in wind forecasting and the practical significance of current developments. In this paper, Optimal Particle Swarm Optimization (PSO) based regression model is used to predict 1 hour wind speed For this study, wind time series data was obtained from the National Renewable Energy Laboratory (NREL) website. In this work, the hourly average 60-minute wind speed data set for 2016 and 2017 has been used for analysis. In this actual and predicted wind speed, the comparison of the entire data with a sub-series approximation and delayed samples is evaluated. After the predictive analysis (average absolute percentage error), the MAPE error is calculated. In this MAPE there are fewer compared to the rest of the methods, and the model is suitable for all seasons of the year with low complexity. This method is more accurate than the remaining methods.
Keywords: Forecasting, Partical Swarm Optimization (PSO), Regression, Wind Speed Series Data.
Scope of the Article: Regression and Prediction