Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030
Foresight of geothermal energy installation is valuable for energy decision-makers, allowing them to readily identify new capacity units, improve existing energy policies and plans, expand future infrastructure, and fulfill consumer load needs. Therefore, in this paper, an improved grey prediction m...
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ftmdpi:oai:mdpi.com:/2673-7264/2/4/23/ 2023-08-20T04:07:32+02:00 Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030 Khaled Salhein C. J. Kobus Mohamed Zohdy 2022-10-09 application/pdf https://doi.org/10.3390/thermo2040023 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/thermo2040023 https://creativecommons.org/licenses/by/4.0/ Thermo; Volume 2; Issue 4; Pages: 334-351 geothermal energy grey prediction model (GM (1,1)) improved grey prediction model (IGM (1,1)) Text 2022 ftmdpi https://doi.org/10.3390/thermo2040023 2023-08-01T06:48:21Z Foresight of geothermal energy installation is valuable for energy decision-makers, allowing them to readily identify new capacity units, improve existing energy policies and plans, expand future infrastructure, and fulfill consumer load needs. Therefore, in this paper, an improved grey prediction model (IGM (1,1)) was applied to perform the annual geothermal energy installation capacity prediction for the top 10 countries based on installed power generation capacity evaluated at the end of 2021, namely the United States, Indonesia, Philippines, Turkey, New Zealand, Mexico, Italy, Kenya, Iceland, and Japan, for the next nine years for the period from 2022 through 2030. These data can be used by future researchers in the field. Separately, datasets from 2000 to 2021 were collected for each country’s geothermal energy installation capacity to build a model which can accurately predict the annually geothermal energy installation capacity by 2030. The IGM (1,1) model used a small dataset of 22 data points, with one point denoting one year (i.e., 22 years), to predict the capacity of geothermal energy installations for the next nine years. Following that, the model was implemented for each dataset in MATLAB, where appropriate, and the model accuracy was evaluated. Ten separate geothermal energy installation capacity datasets were used to validate the improved model, and these datasets further demonstrated the overall improved model’s accuracy. The results prove that the prediction accuracy of the IGM (1,1) model outperforms the benchmark conventional GM (1,1) model, thereby enhancing the overall accuracy of the GM (1,1) model. The IGM (1,1) model ensures error reduction, suggesting that it is an effective and promising tool for accurate short-term prediction. The results reveal the 2030 geothermal energy installation capacity rankings. Text Iceland MDPI Open Access Publishing New Zealand Thermo 2 4 334 351 |
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geothermal energy grey prediction model (GM (1,1)) improved grey prediction model (IGM (1,1)) |
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geothermal energy grey prediction model (GM (1,1)) improved grey prediction model (IGM (1,1)) Khaled Salhein C. J. Kobus Mohamed Zohdy Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030 |
topic_facet |
geothermal energy grey prediction model (GM (1,1)) improved grey prediction model (IGM (1,1)) |
description |
Foresight of geothermal energy installation is valuable for energy decision-makers, allowing them to readily identify new capacity units, improve existing energy policies and plans, expand future infrastructure, and fulfill consumer load needs. Therefore, in this paper, an improved grey prediction model (IGM (1,1)) was applied to perform the annual geothermal energy installation capacity prediction for the top 10 countries based on installed power generation capacity evaluated at the end of 2021, namely the United States, Indonesia, Philippines, Turkey, New Zealand, Mexico, Italy, Kenya, Iceland, and Japan, for the next nine years for the period from 2022 through 2030. These data can be used by future researchers in the field. Separately, datasets from 2000 to 2021 were collected for each country’s geothermal energy installation capacity to build a model which can accurately predict the annually geothermal energy installation capacity by 2030. The IGM (1,1) model used a small dataset of 22 data points, with one point denoting one year (i.e., 22 years), to predict the capacity of geothermal energy installations for the next nine years. Following that, the model was implemented for each dataset in MATLAB, where appropriate, and the model accuracy was evaluated. Ten separate geothermal energy installation capacity datasets were used to validate the improved model, and these datasets further demonstrated the overall improved model’s accuracy. The results prove that the prediction accuracy of the IGM (1,1) model outperforms the benchmark conventional GM (1,1) model, thereby enhancing the overall accuracy of the GM (1,1) model. The IGM (1,1) model ensures error reduction, suggesting that it is an effective and promising tool for accurate short-term prediction. The results reveal the 2030 geothermal energy installation capacity rankings. |
format |
Text |
author |
Khaled Salhein C. J. Kobus Mohamed Zohdy |
author_facet |
Khaled Salhein C. J. Kobus Mohamed Zohdy |
author_sort |
Khaled Salhein |
title |
Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030 |
title_short |
Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030 |
title_full |
Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030 |
title_fullStr |
Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030 |
title_full_unstemmed |
Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030 |
title_sort |
forecasting installation capacity for the top 10 countries utilizing geothermal energy by 2030 |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/thermo2040023 |
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New Zealand |
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New Zealand |
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Iceland |
genre_facet |
Iceland |
op_source |
Thermo; Volume 2; Issue 4; Pages: 334-351 |
op_relation |
https://dx.doi.org/10.3390/thermo2040023 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/thermo2040023 |
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351 |
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