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

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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)

  • GM Shafiullah

    (Discipline of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

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.

Suggested Citation

  • Siddik Shakul Hameed & Ramesh Ramadoss & Kannadasan Raju & GM Shafiullah, 2022. "A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression," Sustainability, MDPI, vol. 14(7), pages 1-29, April.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4235-:d:786016
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    References listed on IDEAS

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    2. Hendra Hendra & Dhimas Satria & Hernadewita Hernadewita & Yozerizal Yozerizal & Frengki Hardian & Ahmed M. Galal, 2023. "Performance of Generator Translation and Rotation on Stroke Length Drive of the Two-Rod Mechanism in Renewable Energy Power Plant," Sustainability, MDPI, vol. 15(7), pages 1-14, March.
    3. Vijayaraja Loganathan & Dhanasekar Ravikumar & Rupa Kesavan & Kanakasri Venkatesan & Raadha Saminathan & Raju Kannadasan & Mahalingam Sudhakaran & Mohammed H. Alsharif & Zong Woo Geem & Junhee Hong, 2022. "A Case Study on Renewable Energy Sources, Power Demand, and Policies in the States of South India—Development of a Thermoelectric Model," Sustainability, MDPI, vol. 14(14), pages 1-29, July.
    4. Shijun Wang & Chun Liu & Kui Liang & Ziyun Cheng & Xue Kong & Shuang Gao, 2022. "Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    5. Bukurije Hoxha & Igor K. Shesho & Risto V. Filkoski, 2022. "Analysis of Wind Turbine Distances Using a Novel Techno-Spatial Approach in Complex Wind Farm Terrains," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    6. Panagiotis Korkidis & Anastasios Dounis, 2023. "Intelligent Fuzzy Models: WM, ANFIS, and Patch Learning for the Competitive Forecasting of Environmental Variables," Sustainability, MDPI, vol. 15(10), pages 1-21, May.

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