Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting
The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/16/5487 |
| _version_ | 1827710971724955648 |
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| author | Federico Divina José Francisco Torres Maldonado Miguel García-Torres Francisco Martínez-Álvarez Alicia Troncoso |
| author_facet | Federico Divina José Francisco Torres Maldonado Miguel García-Torres Francisco Martínez-Álvarez Alicia Troncoso |
| author_sort | Federico Divina |
| collection | DOAJ |
| description | The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared. |
| first_indexed | 2024-03-10T17:47:16Z |
| format | Article |
| id | doaj.art-d974316556f84758882d4bb831a90285 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T17:47:16Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d974316556f84758882d4bb831a902852023-11-20T09:29:10ZengMDPI AGApplied Sciences2076-34172020-08-011016548710.3390/app10165487Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption ForecastingFederico Divina0José Francisco Torres Maldonado1Miguel García-Torres2Francisco Martínez-Álvarez3Alicia Troncoso4Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, SpainData Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, SpainData Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, SpainData Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, SpainData Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, SpainThe electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared.https://www.mdpi.com/2076-3417/10/16/5487time-series forecastingdeep learningevolutionary computationneuroevolution |
| spellingShingle | Federico Divina José Francisco Torres Maldonado Miguel García-Torres Francisco Martínez-Álvarez Alicia Troncoso Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting Applied Sciences time-series forecasting deep learning evolutionary computation neuroevolution |
| title | Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting |
| title_full | Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting |
| title_fullStr | Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting |
| title_full_unstemmed | Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting |
| title_short | Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting |
| title_sort | hybridizing deep learning and neuroevolution application to the spanish short term electric energy consumption forecasting |
| topic | time-series forecasting deep learning evolutionary computation neuroevolution |
| url | https://www.mdpi.com/2076-3417/10/16/5487 |
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