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Article

Predicting Renewable Energy Investment Using Machine Learning

1
Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine, Trinidad and Tobago
2
Department of Computer Science, The University of the West Indies, St. Augustine, Trinidad and Tobago
3
National Institute of Higher Education, Research Science and Technology, Port of Spain, Trinidad and Tobago
*
Author to whom correspondence should be addressed.
Energies 2020, 13(17), 4494; https://doi.org/10.3390/en13174494
Received: 22 July 2020 / Revised: 13 August 2020 / Accepted: 25 August 2020 / Published: 31 August 2020
(This article belongs to the Special Issue Green Network Technologies and Renewable Energy Systems)
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. View Full-Text
Keywords: renewable energy; electricity pricing; machine learning; energy policy; regression; neural network renewable energy; electricity pricing; machine learning; energy policy; regression; neural network
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MDPI and ACS Style

Hosein, G.; Hosein, P.; Bahadoorsingh, S.; Martinez, R.; Sharma, C. Predicting Renewable Energy Investment Using Machine Learning. Energies 2020, 13, 4494. https://doi.org/10.3390/en13174494

AMA Style

Hosein G, Hosein P, Bahadoorsingh S, Martinez R, Sharma C. Predicting Renewable Energy Investment Using Machine Learning. Energies. 2020; 13(17):4494. https://doi.org/10.3390/en13174494

Chicago/Turabian Style

Hosein, Govinda, Patrick Hosein, Sanjay Bahadoorsingh, Robert Martinez, and Chandrabhan Sharma. 2020. "Predicting Renewable Energy Investment Using Machine Learning" Energies 13, no. 17: 4494. https://doi.org/10.3390/en13174494

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