A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

Hamid R. Khosravani, María Del Mar Castilla, Manuel Berenguel, Antonio E. Ruano and Pedro M. Ferreira
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Hamid R. Khosravani: Faculty of Science and Technology, University of Algarve, Campus Gambelas, Faro, Portugal
María Del Mar Castilla: Department of Computer Science, Automatic Control, Robotics and Mechatronics Research Group, University of Almería, Agrifood Campus of International Excellence (ceiA3), CIESOL, Joint Center University of Almería-CIEMAT, Almería, Spain
Manuel Berenguel: Department of Computer Science, Automatic Control, Robotics and Mechatronics Research Group, University of Almería, Agrifood Campus of International Excellence (ceiA3), CIESOL, Joint Center University of Almería-CIEMAT, Almería, Spain
Antonio E. Ruano: Faculty of Science and Technology, University of Algarve, Campus Gambelas, Faro, Portugal
Pedro M. Ferreira: LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal

Energies, 2016, vol. 9, issue 1, 1-24

Abstract: 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.

Keywords: predictive model; electric power demand; neural networks; multi objective genetic algorithm (MOGA); data selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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