Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector
Ensuring the supply of electricity in a reliable and safe way is not an easy task, especially when considering renewable and clean energy generated with wind turbines given the intermittency or variability of the wind; also considering different time horizons increases complexity. Mexico has great p...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/5193336 |
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| _version_ | 1832553716954169344 |
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| author | Inés Romero Alberto Ochoa-Zezzati |
| author_facet | Inés Romero Alberto Ochoa-Zezzati |
| author_sort | Inés Romero |
| collection | DOAJ |
| description | Ensuring the supply of electricity in a reliable and safe way is not an easy task, especially when considering renewable and clean energy generated with wind turbines given the intermittency or variability of the wind; also considering different time horizons increases complexity. Mexico has great potential for wind energy in the Eastern region and, to meet this challenge, a platform capable of generating forecast models automatically through mathematical techniques and artificial intelligence and managing them is proposed aimed at providing support based on knowledge and presenting the information graphically through a flexible dashboard, which is customizable and accessible when and where required. In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. It is built in a modular way with free and open-source software. The results in the energy sector show that it allows focusing on priority activities avoiding rework, ensures reliability and completeness, is scalable, avoids duplication, allows resources to be shared, responds quickly to hypotheses, and has a global and summarized view of relevant data according to the interested party for different periods of time in an agile way, reducing times and offering support to the decision maker. |
| format | Article |
| id | doaj-art-e85917e1b0254651bc1822dacc3c1e15 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-e85917e1b0254651bc1822dacc3c1e152025-02-03T05:53:29ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/5193336Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy SectorInés Romero0Alberto Ochoa-Zezzati1Programa de Desarrollo Sostenible y Energías RenovablesDepartamento/Facultad: Inteligencia Artificial AplicadaEnsuring the supply of electricity in a reliable and safe way is not an easy task, especially when considering renewable and clean energy generated with wind turbines given the intermittency or variability of the wind; also considering different time horizons increases complexity. Mexico has great potential for wind energy in the Eastern region and, to meet this challenge, a platform capable of generating forecast models automatically through mathematical techniques and artificial intelligence and managing them is proposed aimed at providing support based on knowledge and presenting the information graphically through a flexible dashboard, which is customizable and accessible when and where required. In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. It is built in a modular way with free and open-source software. The results in the energy sector show that it allows focusing on priority activities avoiding rework, ensures reliability and completeness, is scalable, avoids duplication, allows resources to be shared, responds quickly to hypotheses, and has a global and summarized view of relevant data according to the interested party for different periods of time in an agile way, reducing times and offering support to the decision maker.http://dx.doi.org/10.1155/2022/5193336 |
| spellingShingle | Inés Romero Alberto Ochoa-Zezzati Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector Journal of Electrical and Computer Engineering |
| title | Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector |
| title_full | Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector |
| title_fullStr | Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector |
| title_full_unstemmed | Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector |
| title_short | Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector |
| title_sort | analytical dynamic and interactive platform for generation and managing predictive models focused on energy sector |
| url | http://dx.doi.org/10.1155/2022/5193336 |
| work_keys_str_mv | AT inesromero analyticaldynamicandinteractiveplatformforgenerationandmanagingpredictivemodelsfocusedonenergysector AT albertoochoazezzati analyticaldynamicandinteractiveplatformforgenerationandmanagingpredictivemodelsfocusedonenergysector |