Predicting Renewable Energy Investment Using Machine Learning

Govinda Hosein, Patrick Hosein, Sanjay Bahadoorsingh, Robert Martinez and Chandrabhan Sharma
Additional contact information
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

Energies, 2020, vol. 13, issue 17, 1-9

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.

Keywords: renewable energy; electricity pricing; machine learning; energy policy; regression; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/17/4494/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/17/4494/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:17:p:4494-:d:406834

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().