A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression

Siddik Shakul Hameed, Ramesh Ramadoss, Kannadasan Raju and Shafiullah Gm
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Siddik Shakul Hameed: Department of Electrical and Electronics Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India
Ramesh Ramadoss: Department of Electrical and Electronics Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India
Kannadasan Raju: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India
Shafiullah Gm: Discipline of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia

Sustainability, 2022, vol. 14, issue 7, 1-29

Abstract: Wind energy is one of the most promising alternates of fossil fuels because of its abundant availability, low cost, and pollution-free attributes. Wind potential estimation, wind forecasting, and effective wind-energy management are the critical factors in planning and managing wind farms connected to wind-pooling substations. Hence, this study proposes a hybrid framework-based approach for wind-resource estimation and forecasting, namely IGWO-SVR (improved grey wolf optimization method (IGWO)-support vector regression (SVR)) for a real-time power pooling substation. The wind resource assessment and behavioral wind analysis has been carried out with the proposed IGWO-SVR optimization method for hourly, daily, monthly, and annual cases using 40 years of ERA (European Center for Medium-Range Weather Forecast reanalysis) data along with the impact of the El Niño effect. First, wind reassessment is carried out considering the impact of El Niño, wind speed, power, pressure, and temperature of the selected site Radhapuram substation in Tamilnadu, India and reported extensively. In addition, statistical analysis and wind distribution fitting are performed to demonstrate the seasonal effect. Then the proposed model is adopted for wind speed forecasting based on the dataset. From the results, the proposed model offered the best assessment report and predicted the wind behavior with greater accuracy using evaluation metrics, namely root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). For short-term wind speed, power, and El Niño forecasting, IGWO-SVR optimization effectively outperforms other existing models. This method can be adapted effectively in any potential locations for wind resource assessment and forecasting needs for better renewable energy management by power utilities.

Keywords: wind energy; wind forecasting; wind potential assessment; IGWO-SVR (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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