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Temperature Sensing Optimization for Home Thermostat Retrofit
Most existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRV...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198709/ https://www.ncbi.nlm.nih.gov/pubmed/34073156 http://dx.doi.org/10.3390/s21113685 |
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author | Seri, Federico Arnesano, Marco Keane, Marcus Martin Revel, Gian Marco |
author_facet | Seri, Federico Arnesano, Marco Keane, Marcus Martin Revel, Gian Marco |
author_sort | Seri, Federico |
collection | PubMed |
description | Most existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRVs) as cheap and relatively easy-to-install retrofit solutions. These systems provide increased freedom of installation, due to the use of wireless communication; however, the uncertainty of the measured air temperature, considering the thermostat placement, could impact the final heating performance. This paper presents a sensing optimization approach for a home thermostat, in order to determine the optimal retrofit configuration to reduce the sensing uncertainty, thus achieving the required comfort level and minimizing the retrofit’s payback period. The methodology was applied to a real case study—a dwelling located in Italy. The measured data and a simulation model were used to create different retrofit scenarios. Among these, the optimal scenario was achieved through thermostat repositioning and a setpoint of 21 °C, without the use of TRVs. Such optimization provided an improvement of control performance due to sensor location, with consequent energy savings of 7% (compared to the baseline). The resulting payback period ranged from two and a half years to less than a year, depending on impact of the embedded smart thermostat algorithms. |
format | Online Article Text |
id | pubmed-8198709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81987092021-06-14 Temperature Sensing Optimization for Home Thermostat Retrofit Seri, Federico Arnesano, Marco Keane, Marcus Martin Revel, Gian Marco Sensors (Basel) Article Most existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRVs) as cheap and relatively easy-to-install retrofit solutions. These systems provide increased freedom of installation, due to the use of wireless communication; however, the uncertainty of the measured air temperature, considering the thermostat placement, could impact the final heating performance. This paper presents a sensing optimization approach for a home thermostat, in order to determine the optimal retrofit configuration to reduce the sensing uncertainty, thus achieving the required comfort level and minimizing the retrofit’s payback period. The methodology was applied to a real case study—a dwelling located in Italy. The measured data and a simulation model were used to create different retrofit scenarios. Among these, the optimal scenario was achieved through thermostat repositioning and a setpoint of 21 °C, without the use of TRVs. Such optimization provided an improvement of control performance due to sensor location, with consequent energy savings of 7% (compared to the baseline). The resulting payback period ranged from two and a half years to less than a year, depending on impact of the embedded smart thermostat algorithms. MDPI 2021-05-26 /pmc/articles/PMC8198709/ /pubmed/34073156 http://dx.doi.org/10.3390/s21113685 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Seri, Federico Arnesano, Marco Keane, Marcus Martin Revel, Gian Marco Temperature Sensing Optimization for Home Thermostat Retrofit |
title | Temperature Sensing Optimization for Home Thermostat Retrofit |
title_full | Temperature Sensing Optimization for Home Thermostat Retrofit |
title_fullStr | Temperature Sensing Optimization for Home Thermostat Retrofit |
title_full_unstemmed | Temperature Sensing Optimization for Home Thermostat Retrofit |
title_short | Temperature Sensing Optimization for Home Thermostat Retrofit |
title_sort | temperature sensing optimization for home thermostat retrofit |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198709/ https://www.ncbi.nlm.nih.gov/pubmed/34073156 http://dx.doi.org/10.3390/s21113685 |
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