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An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation

Author

Listed:
  • Imran Shafi

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Harris Khan

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Muhammad Siddique Farooq

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Isabel de la Torre Diez

    (Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain)

  • Yini Miró

    (Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA)

  • Juan Castanedo Galán

    (Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Research Group on Foods, Nutritional Biochemistry and Health, Universidade Internacional do Cuanza, Cuito, Bié P.O. Box 841, Angola
    Research Group on Foods, Nutritional Biochemistry and Health, Fundación Universitaria Internacional de Colombia, Bogotá 111311, Colombia)

  • Imran Ashraf

    (Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea)

Abstract

The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation.

Suggested Citation

  • Imran Shafi & Harris Khan & Muhammad Siddique Farooq & Isabel de la Torre Diez & Yini Miró & Juan Castanedo Galán & Imran Ashraf, 2023. "An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation," Energies, MDPI, vol. 16(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4171-:d:1149990
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    References listed on IDEAS

    as
    1. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    2. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    3. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    4. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    5. Huang, Qunwu & Shi, Yeqiang & Wang, Yiping & Lu, Linping & Cui, Yong, 2015. "Multi-turbine wind-solar hybrid system," Renewable Energy, Elsevier, vol. 76(C), pages 401-407.
    6. Chang, Tian-Pau & Liu, Feng-Jiao & Ko, Hong-Hsi & Cheng, Shih-Ping & Sun, Li-Chung & Kuo, Shye-Chorng, 2014. "Comparative analysis on power curve models of wind turbine generator in estimating capacity factor," Energy, Elsevier, vol. 73(C), pages 88-95.
    7. Chen, Hung-Cheng, 2013. "Optimum capacity determination of stand-alone hybrid generation system considering cost and reliability," Applied Energy, Elsevier, vol. 103(C), pages 155-164.
    8. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
    9. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    10. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
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