4.6 Article

Temperature Sensing Optimization for Home Thermostat Retrofit

Journal

SENSORS
Volume 21, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/s21113685

Keywords

sensing optimization; thermostat retrofit; building simulation; optimization; thermal comfort; smart thermostat; thermostat placement

Funding

  1. H2020 research and innovation programme [680474]
  2. H2020 Societal Challenges Programme [680474] Funding Source: H2020 Societal Challenges Programme

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The study introduces a sensing optimization approach for home thermostats to reduce sensing uncertainty, achieve comfort levels, and minimize retrofit payback period. Practical application demonstrates that repositioning thermostats and setting appropriate temperatures can improve control performance, save energy, and shorten payback periods.
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 degrees 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.

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