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Predicting Renewable Energy Investment Using Machine Learning

Author

Listed:
  • Govinda Hosein

    (Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine, Trinidad and Tobago)

  • Patrick Hosein

    (Department of Computer Science, The University of the West Indies, St. Augustine, Trinidad and Tobago)

  • Sanjay Bahadoorsingh

    (Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine, Trinidad and Tobago)

  • Robert Martinez

    (National Institute of Higher Education, Research Science and Technology, Port of Spain, Trinidad and Tobago)

  • Chandrabhan Sharma

    (Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine, Trinidad and Tobago)

Abstract

In order to combat climate change, many countries have promised to bolster Renewable Energy (RE) production following the Paris Agreement with some countries even setting a goal of 100% by 2025. The reasons are twofold: capitalizing on carbon emissions whilst concomitantly benefiting from reduced fossil fuel dependence and the fluctuations associated with imported fuel prices. However, numerous countries have not yet made preparations to increase RE production and integration. In many instances, this reluctance seems to be predominant in energy-rich countries, which typically provide heavy subsidies on electricity prices. With such subsidies, there is no incentive to invest in RE since the time taken to recoup such investments would be significant. We develop a model using a Neural Network (NN) regression algorithm to quantitatively illustrate this conjecture and also use it to predict the reduction in electricity price subsidies required to achieve a specified RE production target. The model was trained using 10 leading metrics from 53 countries. It is envisaged that policymakers and researchers can use this model to plan future RE targets to satisfy the Nationally Determined Contributions (NDC) and determine the required electricity subsidy reductions. The model can easily be modified to predict what changes in other country factors can be made to stimulate growth in RE production. We illustrate this approach with a sample use case.

Suggested Citation

  • Govinda Hosein & Patrick Hosein & Sanjay Bahadoorsingh & Robert Martinez & Chandrabhan Sharma, 2020. "Predicting Renewable Energy Investment Using Machine Learning," Energies, MDPI, vol. 13(17), pages 1-9, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4494-:d:406834
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    References listed on IDEAS

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    1. Zheng-Xia He & Shi-Chun Xu & Qin-Bin Li & Bin Zhao, 2018. "Factors That Influence Renewable Energy Technological Innovation in China: A Dynamic Panel Approach," Sustainability, MDPI, vol. 10(1), pages 1-30, January.
    2. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo, 2008. "The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany," Energy Policy, Elsevier, vol. 36(8), pages 3076-3084, August.
    3. Mulder, Machiel & Scholtens, Bert, 2013. "The impact of renewable energy on electricity prices in the Netherlands," Renewable Energy, Elsevier, vol. 57(C), pages 94-100.
    4. A. Gürhan Kök & Kevin Shang & Şafak Yücel, 2018. "Impact of Electricity Pricing Policies on Renewable Energy Investments and Carbon Emissions," Management Science, INFORMS, vol. 64(1), pages 131-148, January.
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    1. Batara Surya & Andi Muhibuddin & Seri Suriani & Emil Salim Rasyidi & Baharuddin Baharuddin & Andi Tenri Fitriyah & Herminawaty Abubakar, 2021. "Economic Evaluation, Use of Renewable Energy, and Sustainable Urban Development Mamminasata Metropolitan, Indonesia," Sustainability, MDPI, vol. 13(3), pages 1-45, January.

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