Predicting Renewable Energy Investment Using Machine Learning
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 fo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/17/4494 |
| _version_ | 1797554962810535936 |
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| author | Govinda Hosein Patrick Hosein Sanjay Bahadoorsingh Robert Martinez Chandrabhan Sharma |
| author_facet | Govinda Hosein Patrick Hosein Sanjay Bahadoorsingh Robert Martinez Chandrabhan Sharma |
| author_sort | Govinda Hosein |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:40:26Z |
| format | Article |
| id | doaj.art-1f37d4c02e9b455593eebd3783a9d71a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:40:26Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1f37d4c02e9b455593eebd3783a9d71a2023-11-20T12:03:51ZengMDPI AGEnergies1996-10732020-08-011317449410.3390/en13174494Predicting Renewable Energy Investment Using Machine LearningGovinda Hosein0Patrick Hosein1Sanjay Bahadoorsingh2Robert Martinez3Chandrabhan Sharma4Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine, Trinidad and TobagoDepartment of Computer Science, The University of the West Indies, St. Augustine, Trinidad and TobagoDepartment of Electrical and Computer Engineering, The University of the West Indies, St. Augustine, Trinidad and TobagoNational Institute of Higher Education, Research Science and Technology, Port of Spain, Trinidad and TobagoDepartment of Electrical and Computer Engineering, The University of the West Indies, St. Augustine, Trinidad and TobagoIn 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.https://www.mdpi.com/1996-1073/13/17/4494renewable energyelectricity pricingmachine learningenergy policyregressionneural network |
| spellingShingle | Govinda Hosein Patrick Hosein Sanjay Bahadoorsingh Robert Martinez Chandrabhan Sharma Predicting Renewable Energy Investment Using Machine Learning Energies renewable energy electricity pricing machine learning energy policy regression neural network |
| title | Predicting Renewable Energy Investment Using Machine Learning |
| title_full | Predicting Renewable Energy Investment Using Machine Learning |
| title_fullStr | Predicting Renewable Energy Investment Using Machine Learning |
| title_full_unstemmed | Predicting Renewable Energy Investment Using Machine Learning |
| title_short | Predicting Renewable Energy Investment Using Machine Learning |
| title_sort | predicting renewable energy investment using machine learning |
| topic | renewable energy electricity pricing machine learning energy policy regression neural network |
| url | https://www.mdpi.com/1996-1073/13/17/4494 |
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