Estimating Adaptive Setpoint Temperatures Using Weather Stations

David Bienvenido-Huertas, Carlos Rubio-Bellido, Juan Luis Pérez-Ordóñez and Fernando Martínez-Abella
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David Bienvenido-Huertas: Department of Graphical Expression and Building Engineering, University of Seville, 41012 Seville, Spain
Carlos Rubio-Bellido: Department of Building Construction II, University of Seville, 41012 Seville, Spain
Juan Luis Pérez-Ordóñez: Department of Civil Engineering, University of A Coruña, E.T.S.I. Caminos, Canales, Puertos Campus Elviña s/n, 15071 La Coruña, Spain
Fernando Martínez-Abella: Department of Civil Engineering, University of A Coruña, E.T.S.I. Caminos, Canales, Puertos Campus Elviña s/n, 15071 La Coruña, Spain

Energies, 2019, vol. 12, issue 7, 1-47

Abstract: Reducing both the energy consumption and CO 2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.

Keywords: adaptive setpoint temperature; weather station; multivariable linear regression; multilayer perceptron (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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