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.

Temperature Sensing Optimization for Home Thermostat Retrofit

Arnesano, Marco;
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/33629
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