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.
Highlights
Due to fast economic development affected by industrialization and globalization, energy consumption has been steadily increasing over the last years [1,2]
The complete model proposed in [17] and the models obtained by multi objective genetic algorithm (MOGA) will be denoted as PREVIOUS
In order to compare the MOGA models obtained from each experiment with the PREVIOUS model, one model was selected from the non-dominated/preferred set, with a good compromise between performance and complexity
Summary
Due to fast economic development affected by industrialization and globalization, energy consumption has been steadily increasing over the last years [1,2]. Energy consumption management is a very significant problem to tackle the losses resulting from increasing consumption patterns and to improve the performance of building energy systems. There has been a focus on bioclimatic architectures for buildings to reduce the indoor consumption of energy. In this kind of architecture, buildings are designed based on the local climate conditions. Physical properties of buildings are considered in bioclimatic architectures, such as shape, buildings’ orientation related to the sun and wind, wall thickness and roof construction [6,9]
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