A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building
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Author/sKhosravani, Hamid R.; Castilla Nieto, María del Mar; Berenguel Soria, Manuel; Ruano, Antonio E.; Ferreira, Pedro M.
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 desig...
electric power demand
multi objective genetic algorithm (MOGA)