A Systematic Study for Smart Residential Thermostats: User Needs for the Input, Output, and Intelligence Level

Rau, Pei-Luen Patrick; Gong, Yun; Huang, Han-Jing; Wen, Jing

doi:10.3390/buildings6020019

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Many efforts in research and industrial development move towards the design and manufacturing of cheap, friendly and easy-to-use smart thermostats. P. Rau et al presented a study of the needs of these thermostats obtained from interviews and surveys of the consumers [9], noticing that there are still unsolved problems in the hardware and technology on this type of devices and advising the researchers and designers to focus on the user's needs. Some other reviews and detailed studies of modern thermostats can be specified [10,11] which indicate that a lot of work still has to be done in order to clearly see the benefits of this smart management of temperature.…”

Section: State-of-the-artmentioning

confidence: 99%

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Smart Thermostats: An Experimental Facility to Test Their Capabilities and Savings Potential

et al. 2017

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Abstract:The European Commission has explained how heating and cooling in buildings and industry account for half of the energy consumption of the EU. Several studies explain how to achieve an energy saving at home, and the use of smart thermostats will help to reduce energy consumption while increasing the efficiency of households. In this article, a comparative evaluation was carried out between four smart thermostats that are now on the market, whose characteristics vary in terms of price, precision of measurements and set temperature, algorithms, etc. A thermal test chamber was designed and constructed from a refrigerator, a thermal blanket, a Raspberry Pi and the necessary electronic components for its control and data collection. From the tests carried out in the thermal chamber, data on the operation of the four thermostats such as the maintenance and the anticipation of the setpoint temperature, were obtained. It was necessary to run the system enough times for each thermostat to memorize the housing characteristics, such as its inertia and its thermal insulation. This would also allow for the generation of a better algorithm to regulate the temperature, which would create a lower oscillation with respect to the setpoint temperature. The learning of the thermostats was not demonstrated and for the anticipation mode it was seen that the thermostats failed to improve or learn in this aspect, as they did not improve the start-up times of the heating system, with the consequent increase in energy consumption.

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Section: State-of-the-artmentioning

confidence: 99%

“…Also, the general perception from the user's answers in the surveys and interviews shows the difficulty of knowing the real savings coming from the use of smart thermostats [9,11]. These were the original motivations of this new research.…”

Section: State-of-the-artmentioning

confidence: 99%

Smart Thermostats: An Experimental Facility to Test Their Capabilities and Savings Potential

et al. 2017

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“…However, the more intelligent the system is the more complex it gets [4]. A survey by Rau et al [5] shows the main issues with smart thermostats and people reaction about them. Various methodologies and devices have been developed and introduced during the last two decades to reduce energy consumption for residential and commercial buildings.…”

Section: List Of Tablesmentioning

confidence: 99%

A virtual thermostat for local temperature control

Alhashme

2016

Energy and Buildings

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This thesis presents design and operation of a newly developed virtual thermostat to control any desired local temperature in an HVAC controlled space. Conventional HVAC thermostats use a single point temperature sensor at one fixed location in a controlled space to control the temperature of the whole space. The single point temperature sensor does not represent the entire controlled domain. In addition, in most applications only a small zone in the controlled space needs to be controlled (for example where the people are located). Heating and cooling of the unnecessary zones lead to extra energy consumption that can be saved if only the required zones are controlled. Such a control system requires knowledge of the temperature distribution in the whole zone at all times. In this work, we have used computational fluid dynamics (CFD) to determine the distributive temperature inside the controlled space at all times and turn the HVAC system ON and OFF based on the temperature of the desired zone in the space. In this study, we have determined the energy savings by comparing the energy consumption of a conventional thermostat controlled system with a locally controlled one. Several cases with different heat losses, and different inlet air velocities and directions are studied. Energy savings of up to 22% is realized for the cases studied here. In addition, the study showed the effect of person's location on the energy saving using the local control. One of the main problems with such a control system is that the CFD calculations may take a long time (10-24 hours depending on the complexity of the iii problem), which is not practical for real-time temperature control. In order to reduce the computational times and simulate various operating conditions quickly, an artificial neural network (ANN) model is used to train the temperature control system. This resulted in reduction of the computational times to few seconds, making this system practical. This thesis, presents various elements of the newly developed virtual thermostat and its operating principals.iv

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A user-interactive system for smart thermal environment control in office buildings

Liu¹,

Lee²,

Bilionis³

et al. 2021

Applied Energy

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A Systematic Study for Smart Residential Thermostats: User Needs for the Input, Output, and Intelligence Level

Cited by 5 publications

References 20 publications

Smart Thermostats: An Experimental Facility to Test Their Capabilities and Savings Potential

Smart Thermostats: An Experimental Facility to Test Their Capabilities and Savings Potential

A virtual thermostat for local temperature control

A user-interactive system for smart thermal environment control in office buildings

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