Estimating Adaptive Setpoint Temperatures Using Weather Stations
Reducing both the energy consumption and CO<sub>2</sub> 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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/7/1197 |
| _version_ | 1828138987702714368 |
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| author | David Bienvenido-Huertas Carlos Rubio-Bellido Juan Luis Pérez-Ordóñez Fernando Martínez-Abella |
| author_facet | David Bienvenido-Huertas Carlos Rubio-Bellido Juan Luis Pérez-Ordóñez Fernando Martínez-Abella |
| author_sort | David Bienvenido-Huertas |
| collection | DOAJ |
| description | Reducing both the energy consumption and CO<sub>2</sub> 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. |
| first_indexed | 2024-04-11T18:44:59Z |
| format | Article |
| id | doaj.art-33f8a698952c431093f33b839a7c6beb |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:44:59Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-33f8a698952c431093f33b839a7c6beb2022-12-22T04:08:50ZengMDPI AGEnergies1996-10732019-03-01127119710.3390/en12071197en12071197Estimating Adaptive Setpoint Temperatures Using Weather StationsDavid Bienvenido-Huertas0Carlos Rubio-Bellido1Juan Luis Pérez-Ordóñez2Fernando Martínez-Abella3Department of Graphical Expression and Building Engineering, University of Seville, 41012 Seville, SpainDepartment of Building Construction II, University of Seville, 41012 Seville, SpainDepartment of Civil Engineering, University of A Coruña, E.T.S.I. Caminos, Canales, Puertos Campus Elviña s/n, 15071 La Coruña, SpainDepartment of Civil Engineering, University of A Coruña, E.T.S.I. Caminos, Canales, Puertos Campus Elviña s/n, 15071 La Coruña, SpainReducing both the energy consumption and CO<sub>2</sub> 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.https://www.mdpi.com/1996-1073/12/7/1197adaptive setpoint temperatureweather stationmultivariable linear regressionmultilayer perceptron |
| spellingShingle | David Bienvenido-Huertas Carlos Rubio-Bellido Juan Luis Pérez-Ordóñez Fernando Martínez-Abella Estimating Adaptive Setpoint Temperatures Using Weather Stations Energies adaptive setpoint temperature weather station multivariable linear regression multilayer perceptron |
| title | Estimating Adaptive Setpoint Temperatures Using Weather Stations |
| title_full | Estimating Adaptive Setpoint Temperatures Using Weather Stations |
| title_fullStr | Estimating Adaptive Setpoint Temperatures Using Weather Stations |
| title_full_unstemmed | Estimating Adaptive Setpoint Temperatures Using Weather Stations |
| title_short | Estimating Adaptive Setpoint Temperatures Using Weather Stations |
| title_sort | estimating adaptive setpoint temperatures using weather stations |
| topic | adaptive setpoint temperature weather station multivariable linear regression multilayer perceptron |
| url | https://www.mdpi.com/1996-1073/12/7/1197 |
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