Smart Wi-Fi physics-informed thermostat enabled estimation of residential passive solar heat gain for any residence

Sun, Qiancheng; Alhamayani, Abdulelah D.; Huang, Kefan; Lu, Hao; Ghareeb, Ahmed

doi:10.1016/j.enbuild.2022.111934

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…It is complex to figure out the time that a residence is shaded by a tree or surrounded residence. However, the study developed by Sun et al [15] can be modified to let smart thermostats understand when the house is receiving solar irradiances or not, which could help this proposed technology overcome this limitation. Other limitations, such as the number and type of windows, can also be overcome by utilizing the same work done by Sun et al [15] with knowledge of the total solar transmitted into the house and using in estimating the MRT value for the whole residence.…”

Section: Discussionmentioning

confidence: 99%

“…Table 1 documents the building characteristics of the residences used in this study. [15], and Lou et al [13] developed models from archived time smart Wi-Fi measurements. The analysis described here uniquely considers possible inputs to the model described in Section 3.1.2, moving averages of the smart Wi-Fi measured indoor temperature and relative humidity, as well as the cooling demand status.…”

Section: Methodsmentioning

confidence: 99%

“…Further, they showed that the extent of the savings clearly depended upon the amount of insulation in a residence and exterior shading from other buildings and from trees [14]. A very recent study by Sun et al [15] demonstrated solar heat gain to the same housing set considered by Lou et al [13] and Alhamayani et al [14] is responsible for up to 72% of the cooling load in the residences considered [15].…”

Section: Introductionmentioning

confidence: 96%

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An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data

2022

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The net-zero global carbon target for 2050 needs both expansion of renewable energy and substantive energy consumption reduction. Many of the solutions needed are expensive. Controlling HVAC systems in buildings based upon thermal comfort, not just temperature, uniquely offers a means for deep savings at virtually no cost. In this study, a more accurate means to quantify the savings potential in any building in which smart WiFi thermostats are present is developed. Prior research by Alhamayani et al. leveraging such data for individual residences predicted cooling energy savings in the range from 33 to 47%, but this research was based only upon a singular data-based model of indoor temperature. The present research improves upon this prior research by developing LSTM neural network models for both indoor temperature and humidity. Validation errors are reduced by nearly 22% compared to the prior work. Simulations of thermal comfort control for the residences considered yielded potential savings in the range of 29–43%, dependent upon both solar exposure and insulation characteristics of each residence. This research paves the way for smart Wi-Fi thermostat-enabled thermal comfort control in buildings of all types.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data

2022

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“…The determination of the total energy transferred to the adsorbent bed throughout the desorption process involves evaluating the energy delivered within small-time intervals and summing those values for all intervals. This calculation can be performed using Equation (3).…”

Section: Data Collection Process For the Silica Gel-water Chillermentioning

confidence: 99%

“…Silica–gel adsorption water cooling systems are widely studied for solar cooling systems. [ 1–4 ] It is adopted by many researchers due to its ability to adsorb a very high amount of water vapor, coupled with its affordable price. In addition, attention should be paid to the design of the chillers and the time variation of the chilled water outlet temperature.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Chilled and Hot Water Inlet Temperature, and Mass Flow Rate Influence on Energy and Exergy Efficiency in a Prismatic Adsorbent Bed Cooling Systems

Shakir,

Ghareeb,

Güngör

2023

Energy Tech

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Adsorption technologies offer a promising solution for managing high electricity demand in building by leveraging waste heat recovery and renewable energy sources. The prismatic adsorbent bed adsorption water cooling system investigates energy and exergy analyses for cooling application. With hot water temperature of 80 °C, cooling water inlet temperature 30 °C, and chilled water inlet temperature 14 °C, the system achieves an energy efficiency of 45.2% while demonstrating an energy storage density of ≈340 kJ kg−1. As a result, the exergy efficiency reaches 12.2%, and the mass fraction at the end of the adsorption process is a significant value of 0.13kgadsorbate/kgadsorbent. Increasing the water mass flow rate in the evaporator results in a decrease in exergy efficiency, while simultaneously causing an increase in energy efficiency. With an increase in the chilled water inlet temperature of the evaporator and the hot water inlet temperature of the adsorbent bed, there is an increase in the energy storage density, energy efficiency, and exergy destruction due to irreversibility and losses. According to this study, the impact of the chilled water inlet temperature on the energy and exergy performances of the evaporator is more significant compared to the effect of the water mass flow rate.

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The impact of window orientation, glazing, and window-to-wall ratio on the heating and cooling energy of an office building: The case of hot and semi-arid climate

Ahmed,

Suwaed,

Shakir

et al. 2023

Journal of Engineering Research

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Smart Wi-Fi physics-informed thermostat enabled estimation of residential passive solar heat gain for any residence

Cited by 4 publications

References 21 publications

An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data

An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data

Experimental Investigation of Chilled and Hot Water Inlet Temperature, and Mass Flow Rate Influence on Energy and Exergy Efficiency in a Prismatic Adsorbent Bed Cooling Systems

The impact of window orientation, glazing, and window-to-wall ratio on the heating and cooling energy of an office building: The case of hot and semi-arid climate

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