How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study

Pang, Zhihong; Chen, Yan; Zhang, Jian; O’Neill, Zheng; Cheng, Hwakong; Dong, Bing

doi:10.1016/j.apenergy.2020.116251

Cited by 44 publications

(15 citation statements)

References 28 publications

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“…The study found that adjusting the set point during unoccupied periods has a higher savings ratio in hot climates zones, though cold climates can save via this method. 25 Ultimately, the greatest savings appear to be simply a result of the depth of set point setbacks 25,26 though the magnitude of savings can vary based on several external factors. For most of the cases in the US study, 2°C of setback during the occupied period could result in a 20% reduction and 40% in extreme cases.…”

Section: Evidence To Datementioning

confidence: 99%

“…For most of the cases in the US study, 2°C of setback during the occupied period could result in a 20% reduction and 40% in extreme cases. 25 One German study considered smart thermostats a beneficial low-cost investment opportunity to reduce heating energy consumption in dwellings that have a low to medium efficiency standard and can be an attractive option for tenants who have limited options for improving the energy efficiency in their home. 27 One concern from the reviews is that all field studies in smart thermostats have been over short periods of observation where long-term drifts in energy demand and potential change in occupant interest were not observed.…”

Section: Energy Implicationsmentioning

confidence: 99%

“…According to three leading smart thermostat websites, energy savings can range from £115–130 per year in the UK with no evidence provided, 21, 22 10–15% or $131–145 (£95–105) in the US citing three US studies 23 and up to 23% on annual energy costs when contrasted with a constant 22°C hold. 24 Based on a review of studies performed in the US, Pang, Chen 25 found potential energy savings ranging from 2–22%. However, despite the many studies, Pang, Chen 25 found the following questions unanswered: What is the impact of occupant temperature preference?…”

Section: Evidence To Datementioning

confidence: 99%

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Building performance evaluation of low-energy dwellings with and without smart thermostats

Gupta

Gregg

2022

Building Services Engineering Research and Technology

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Smart thermostats allow continuous learning, remote scheduling and control of indoor temperature. This paper empirically evaluates indoor environmental conditions, occupant experiences and prevalence of summertime overheating in three low-energy dwellings with smart thermostats and compares the results with three similar dwellings with standard programmable thermostats. The study uses building performance evaluation methods combining time-series data on temperature, relative humidity and window opening with survey data on occupant perception of thermal comfort and heating control over the period 2019–2020. While there was little difference observed in the measured and perceived indoor temperatures between dwellings with and without smart thermostats, the six dwellings were different in the way they heated their homes and controlled their indoor environment. A wide indoor temperature range of 16oC–22oC was observed in dwellings with smart thermostats during the heating season. The majority of dwellings also experienced summertime overheating with temperatures in bedrooms going up to 34oC. Individual heating preferences dominated the use of smart or standard thermostats ranging from Cool Conserver, On-off Switcher to On-demand Sizzler. It is vital that energy models consider a range of heating preferences to avoid a gap between expectation and reality. Practical application: Actual in-use performance of dwellings with smart thermostats is necessary for their large-scale deployment. A wide range of thermostat behaviours are documented; therefore, it is vital that energy models consider a range of heating preferences to minimise the gap between energy models and reality. As smart home appliances and controls become more commonplace, the findings demonstrate their need for resident training and trouble-shooting support to ensure smart thermostats deliver their expected benefits. Since most of the case study dwellings experienced summertime overheating, it is also vital that building design tackles overheating through passive measures.

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Section: Evidence To Datementioning

confidence: 99%

Section: Energy Implicationsmentioning

confidence: 99%

Section: Evidence To Datementioning

confidence: 99%

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Building performance evaluation of low-energy dwellings with and without smart thermostats

Gupta

Gregg

2022

Building Services Engineering Research and Technology

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“…Thermostat settings vary quite a bit throughout a single day, across different day types, and for the different behavioral scenarios. To describe how temperature settings were selected for this study, temperature set points used or recommended by various sources are presented in Table 7 based upon research by Pang et al (2020). The settings used in those studies are illustrated in Figure 4, along with the assumed comfort preferences that were selected to be used in this study ("NREL").…”

Section: Thermostatmentioning

confidence: 99%

Modeling Savings for ENERGY STAR Smart Home Energy Management Systems

Hendron

Heinemeier

German

et al. 2021

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“…For example, global variable renewable energy deployment has increased rapidly, with double-digit annual growth rates over the last few decades [2]. Smart devices are also being implemented in homes to control the home equipment to save energy [3]. To accommodate for these advancements, a new framework is needed to operate the grid more efficiently: transactive energy.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings

Pattawi

Munankarmi

Blonsky

et al. 2021

ASME 2021 15th International Conference on Energy Sustainability

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Residential buildings, accounting for 37% of the total electricity consumption in the United States, are suitable for demand response (DR) programs to support effective and economical operation of the power system. A home energy management system (HEMS) enables residential buildings to participate in such programs, but it is also important for HEMS to account for occupant preferences to ensure occupant satisfaction. For example, people who prefer a higher thermal comfort level are likely to consume more energy. In this study, we used foresee™, a HEMS developed by the National Renewable Energy Lab (NREL), to perform a sensitivity analysis of occupant preferences with the following objectives: minimize utility cost, minimize carbon footprint, and maximize thermal comfort. To incorporate the preferences into the HEMS, the SMARTER method was used to derive a set of weighting factors for each objective. We performed week-long building energy simulations using a model of a home in Fort Collins, Colorado, where there is mandatory time-of-use electricity rate structure. The foresee™ HEMS was used to control the home with six different sets of occupant preferences. The study shows that occupant preferences can have a significant impact on energy consumption and is important to consider when modeling residential buildings. Results show that the HEMS could achieve energy reduction ranging from 3% to 21%, cost savings ranging from 5% to 24%, and carbon emission reduction ranging from 3% to 21%, while also maintaining a low thermal discomfort level ranging from 0.78 K-hour to 6.47 K-hour in a one-week period during winter. These outcomes quantify the impact of varying occupant preferences and will be useful in controlling the electrical grid and developing HEMS solutions.

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How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study

Cited by 44 publications

References 28 publications

Building performance evaluation of low-energy dwellings with and without smart thermostats

Building performance evaluation of low-energy dwellings with and without smart thermostats

Modeling Savings for ENERGY STAR Smart Home Energy Management Systems

Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings

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