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Renewable Energy Potential Estimation Using Climatic-Weather-Forecasting Machine Learning Algorithms

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  • Chika Maduabuchi

    (Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
    Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria)

  • Chinedu Nsude

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
    Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA)

  • Chibuoke Eneh

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
    Department of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA)

  • Emmanuel Eke

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
    Laboratory of Industrial Electronics and New Energy Systems (LIEPNES), University of Nigeria Nsukka, Nsukka 410001, Nigeria)

  • Kingsley Okoli

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
    Department of Computer Science and Knowledge Discovery, Saint Petersburg Electrotechnical University LETI, Saint Petersburg 197022, Russia)

  • Emmanuel Okpara

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria)

  • Christian Idogho

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria)

  • Bryan Waya

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria)

  • Catur Harsito

    (Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
    Mechanical Engineering Department, Vocational School of Universitas Sebelas Maret, Surakarta 57126, Indonesia)

Abstract

The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weather parameters that can be used by manufacturing companies in evaluating the profitability of siting renewable energy systems in the region. Crucial weather parameters such as daily air temperature, relative humidity, atmospheric pressure, wind speed, and rainfall were obtained from NASA for a period of 19 years (viz. 2004–2022), resulting in the collection of 6664 high-resolution data points. These data were used to build diverse regressive neural networks with varying hyperparameters to find the best network arrangement. In summary, a low mean-squared error of 7 × 10 −3 and high regression correlations of 96% were obtained during the training.

Suggested Citation

  • Chika Maduabuchi & Chinedu Nsude & Chibuoke Eneh & Emmanuel Eke & Kingsley Okoli & Emmanuel Okpara & Christian Idogho & Bryan Waya & Catur Harsito, 2023. "Renewable Energy Potential Estimation Using Climatic-Weather-Forecasting Machine Learning Algorithms," Energies, MDPI, vol. 16(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1603-:d:1058749
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    References listed on IDEAS

    as
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    4. Adaramola, Muyiwa S., 2012. "Estimating global solar radiation using common meteorological data in Akure, Nigeria," Renewable Energy, Elsevier, vol. 47(C), pages 38-44.
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