Discovering, processing and consolidating housing stock and smart thermostat data in support of energy end-use mapping and housing retrofit program planning

HosseiniHaghighi, SeyedehRabeeh; Panchabikesan, Karthik; Dabirian, Sanam; Webster, Jessica Wagner; Ouf, Mohamed; Eicker, Ursula

doi:10.1016/j.scs.2021.103640

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…However, the detailed information is not always available, especially for older, existing residential buildings. Thus, the availability of data from the smart thermostat is significant to create better residential models [36][37][38].…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Impact of Using a Smart Thermostat and Smart Meter Data on a Whole-Building Energy Simulation

Baltazar

Haberl

2022

Sustainability

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Building energy simulation models have been used to assist the design and/or optimization of buildings energy performance. The results from building energy simulation models can be more reliable when measured energy use data, indoor environmental condition data, system operation status, and coincident weather data are used to validate the simulation results. In this paper, given the wide-spread use of home automation devices in residential buildings, we studied how well a residential building energy simulation model can be tuned using measured interval data from a smart thermostat and smart meter. The analysis is based on a multi-stage approach that can help improve the reliability of the use of building energy simulation models that reflect both the indoor air temperature and whole-building energy use. Results from changing the input parameters in the building simulation show that the comparison of the simulated and measured indoor temperatures fall in a range below a NMBE of 1.5% and a CV-RMSE of 2.2%, while the simulated whole-building energy use matches the measured energy use below a NMBE of −2.7% and a CV-RMSE of 10.9%. We found that the most significant parameters for the indoor air temperature and whole-building energy use were the effective U-value for the slab-on-grade floor and the heating and cooling system operation status, respectively.

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Section: Discussionmentioning

confidence: 99%

Assessment of the Impact of Using a Smart Thermostat and Smart Meter Data on a Whole-Building Energy Simulation

Baltazar

Haberl

2022

Sustainability

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“…Hosseinihaghighi et al utilized digital twins for planning a housing retrofit program. In their study, the technology was used to develop inputs for housing energy modeling and mapping using various data such as heating permits and smart thermostat data [24]. Digital twin technology has also been applied as a method to regulate smart energy systems in housing areas [25,26].…”

Section: Literature Reviewmentioning

confidence: 99%

Mitigating the Energy Consumption and Carbon Emissions of a Residential Area in a Tropical City Using Digital Twin Technology: A Case Study of Bertam, Penang

Mohamad Zaidi,

Lim,

Razali

2024

Buildings

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As of 2022, roughly 79.8% of Malaysia’s population resides in urban areas, increasing the population density of its cities. The hot and humid climate in Malaysia necessitates the constant use of air conditioning, especially in cities, resulting in high residential electric consumption and carbon emissions. The residential sector significantly contributes to global climate change, accounting for 27% of global energy consumption and 17% of carbon emissions. To address this concern, the local framework of the National Low Carbon Cities Masterplan (NLCCM) is advocating for a minimum 33% reduction in carbon emissions by 2030 in urban settings, aligning with the commitments made at the 15th Conference of Parties (COP 15). The aim of this study was to determine the energy consumption and carbon emission of residential areas in a tropical city, as well as explore potential energy and carbon savings. Utilizing the Intelligent Communities Lifecycle–Intelligent Community Design (iCL-iCD) energy simulation software, a digital twin of Bertam City, Penang, was developed. This digital model included 65.4% residential housing, which, in the specific scenario under study, accounted for 36% of the city’s electrical energy consumption for cooling purposes only. An early simulation of the residential areas of the city estimated the baseline energy consumption and carbon emissions to be 607 GWh and 314,736 tCO2e, respectively. Several energy-efficient measures were applied to the residential area of Bertam City, revealing a potential saving of 37.3% in both energy and carbon emissions.

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“…However, none of these studies explored the potential of identifying representative occupancy schedules in residential buildings. Lastly, analyses of occupancy schedules via smart thermostats will contribute to energy simulations at various scales [29,30].…”

Section: Development Of Occupancy Sensing and Modelingmentioning

confidence: 99%

Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator

Jung

Wang

Hong

et al. 2023

Building and Environment

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Discovering, processing and consolidating housing stock and smart thermostat data in support of energy end-use mapping and housing retrofit program planning

Cited by 14 publications

References 18 publications

Assessment of the Impact of Using a Smart Thermostat and Smart Meter Data on a Whole-Building Energy Simulation

Assessment of the Impact of Using a Smart Thermostat and Smart Meter Data on a Whole-Building Energy Simulation

Mitigating the Energy Consumption and Carbon Emissions of a Residential Area in a Tropical City Using Digital Twin Technology: A Case Study of Bertam, Penang

Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator

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