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Prediction of Temperature in Buildings Using Machine Learning Techniques

Prediction of Temperature in Buildings Using Machine Learning Techniques

Juan José Carrasco, Juan Caravaca, Mónica Millán-Giraldo, Gonzalo Vergara, José M. Martínez-Martínez, Javier Sanchis, Emilio Soria-Olivas
Copyright: © 2017 |Pages: 19
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch121
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MLA

Carrasco, Juan José, et al. "Prediction of Temperature in Buildings Using Machine Learning Techniques." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 2901-2919. https://doi.org/10.4018/978-1-5225-1759-7.ch121

APA

Carrasco, J. J., Caravaca, J., Millán-Giraldo, M., Vergara, G., Martínez-Martínez, J. M., Sanchis, J., & Soria-Olivas, E. (2017). Prediction of Temperature in Buildings Using Machine Learning Techniques. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 2901-2919). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch121

Chicago

Carrasco, Juan José, et al. "Prediction of Temperature in Buildings Using Machine Learning Techniques." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 2901-2919. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch121

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Abstract

Energy efficiency is a trend due to ecological and economic benefits. Within this field, energy efficiency in buildings sector constitutes one of the main concerns due to the fact that approximately 40% of total world energy consumption corresponds to this sector. Climate control in buildings has the potential to increase its energy efficiency planning strategies for the heating, ventilation and air conditioning (HVAC) machines. These planning strategies may include a stage for long term indoor temperature forecasting. This chapter entails the use of four prediction models (NAÏVE, MLR, MLP, FIS and ANFIS) to forecast temperature in an office building using a temporal horizon of several hours. The obtained results show that the MLP outperforms the other analyzed models. Finally, the obtained predictors are deeply analyzed to obtain information about the influence of the HVAC settings in the building temperature.

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