A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building
Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7369 |
| _version_ | 1789406478289862656 |
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| author | Khosravani, Hamid R. Castilla Nieto, María del Mar Berenguel Soria, Manuel Ruano, Antonio E. Ferreira, Pedro M. |
| author_facet | Khosravani, Hamid R. Castilla Nieto, María del Mar Berenguel Soria, Manuel Ruano, Antonio E. Ferreira, Pedro M. |
| author_sort | Khosravani, Hamid R. |
| collection | DSpace |
| description | Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7369 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-73692023-04-12T19:25:39Z A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building Khosravani, Hamid R. Castilla Nieto, María del Mar Berenguel Soria, Manuel Ruano, Antonio E. Ferreira, Pedro M. predictive model electric power demand neural networks multi objective genetic algorithm (MOGA) data selection Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity. 2020-01-16T11:00:57Z 2020-01-16T11:00:57Z 2016-01-20 info:eu-repo/semantics/article 1996-1073 http://hdl.handle.net/10835/7369 en https://www.mdpi.com/1996-1073/9/1/57 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | predictive model electric power demand neural networks multi objective genetic algorithm (MOGA) data selection Khosravani, Hamid R. Castilla Nieto, María del Mar Berenguel Soria, Manuel Ruano, Antonio E. Ferreira, Pedro M. A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building |
| title | A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building |
| title_full | A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building |
| title_fullStr | A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building |
| title_full_unstemmed | A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building |
| title_short | A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building |
| title_sort | comparison of energy consumption prediction models based on neural networks of a bioclimatic building |
| topic | predictive model electric power demand neural networks multi objective genetic algorithm (MOGA) data selection |
| url | http://hdl.handle.net/10835/7369 |
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