Energy saving impact of occupancy-driven thermostat for residential buildings

Wang, Chenli; Pattawi, Kaleb; Lee, Hohyun

doi:10.1016/j.enbuild.2020.109791

Cited by 63 publications

(18 citation statements)

References 31 publications

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“…Reference [20] used a university office to compare a rulebased occupancy-reactive control with a model predictive control (MPC) based control that also used occupancy information. As for simulation study of the occupancy-reactive control, [6] evaluated their occupancy-reactive control based on the actual occupancy data and [24] developed an occupancy simulator for including random behavior of occupancy into energy simulation.…”

Section: Related Workmentioning

confidence: 99%

“…Occupancy-driven heating, ventilation, and air-conditioning (HVAC) controls for energy-saving have been studied for more than a decade [5]- [24]. Occupancy-driven HVAC con-trols regulate HVAC systems based on occupancy information, which is calculated from data from sensing devices such as passive infrared (PIR) motion sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Occupancy-driven HVAC con-trols regulate HVAC systems based on occupancy information, which is calculated from data from sensing devices such as passive infrared (PIR) motion sensors. The occupancydriven HVAC controls can be categorized as occupancyreactive controls [6], [10], [14], [16], [20], [21], [24] and occupancy-predictive controls [7]- [9], [11]- [13], [15], [17]- [19], [22], [23]. The former is based on changes in occupancy state of a target space such as a room or house.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Field Study on Actual Usage of Occupancy-Reactive Space Heating Control

Yano

Imahara

2021

IEEE Access

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We present evaluation of actual use of an occupancy-reactive space heating control, which changes set-point temperatures in space heating for energy-savings based on changes in occupancy state. We performed an experiment over two winter months in Lyon, France. In this experiment, occupants were provided with occupancy-reactive and pre-scheduled controls via a home energy management system (HEMS) and they were also allowed to control space heating manually via a thermostat or the HEMS. Occupants decided which control to use from among the two advanced controls and manual control, whereas control availability was limited in some experimental periods. To grasp actual usage of the two advanced controls, we introduce energy-saving potential for valve-regulated space heating and determine numbers of frequent users who applied any of the occupancy-reactive, the pre-scheduled or manual control. We also analyze actual energy consumption of space heating of the frequent users of each control. Our findings suggest energy-saving effects by the occupancy-reactive control, but the results show that the number of the occupancy-reactive control users in the experiment was not so large. This observation encourages reconsideration of the assumption that advanced controls such as the occupancy-reactive control are used fully by occupants in previous studies, indicating a necessity for promoting comprehension and active use of occupancy-reactive controls. INDEX TERMS Heating ventilation and airconditioning (HVAC), home energy management system (HEMS), occupancy-reactive space heating control.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Field Study on Actual Usage of Occupancy-Reactive Space Heating Control

Yano

Imahara

2021

IEEE Access

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“…This would be a easy addition to the model by adding another set of coefficients to our equation and doing another grid search for that new model parameter. The methodology described in [27,28], using occupancy based control would be a great implementation to our model for load actuation. This of course increases the model size in terms of having to search for more than three coefficients but now having to search for four or five, however this will still be faster than any EnergyPlus simulation.…”

Section: Future Workmentioning

confidence: 99%

Control-Oriented, Data-Driven Models of Thermal Dynamics

Boskic¹,

Korda²,

Mezić³

2021

Energies

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We investigate data-driven, simple-to-implement residential environmental models that can serve as the basis for energy saving algorithms in both retrofits and new designs of residential buildings. Despite the nonlinearity of the underlying dynamics, using Koopman operator theory framework in this study we show that a linear second order model embedding, that captures the physics that occur inside a single or multi zone space does well when compared with data simulated using EnergyPlus. This class of models has low complexity. We show that their parameters have physical significance for the large-scale dynamics of a building and are correlated to concepts such as the thermal mass. We investigate consequences of changing the thermal mass on the energy behavior of a building system and provide best practice design suggestions.

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“…In most recent research works, the value of 60% is tossed as a reasonable number to describe a vacant or occupied zone. Wang et al presented a co-simulation platform to assess the occupancy-driven thermostat's energy-saving impact and economic benefits in a typical single-family residential building [28]. They devised an occupancy simulator and utilized it to consider the occupancy's random nature based on three types of thermostats (always on, schedule-based, and occupancy-driven).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

2021

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Cooling systems play a key role in maintaining human comfort inside buildings. The key challenges that are facing conventional cooling systems are the rapid growth of total cooling energy and annual electricity consumption in commercial buildings. This is even more significant in countries with an arid climate, where the cooling systems are typically working 80% of the year. Thus, there has been growing interest in developing smart control models to assign optimal cooling setpoints in recent years. In the present work, we propose an occupancy-based control model that is based on a non-linear optimization algorithm to efficiently reduce energy consumption and costs. The model utilizes a Monte-Carlo method to determine the approximate occupancy schedule for building thermal zones. We compare our proposed model to three different strategies, namely: always-on thermostat, schedule-based model, and a rule-based occupancy-driven model. Unlike these three rule-based algorithms, the proposed optimization approach is a white-box model that considers the thermodynamic relationships in the cooling system to find the optimal cooling setpoints. For comparison, different case studies in five cities around the world were investigated. Our findings illustrate that the proposed optimization algorithm is able to noticeably reduce the cooling energy consumption of the buildings. Significantly, in cities that were located in severe hot regions, such as Doha and Phoenix, cooling energy consumption can be reduced by 14.71% and 15.19%, respectively.

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Energy saving impact of occupancy-driven thermostat for residential buildings

Cited by 63 publications

References 31 publications

Field Study on Actual Usage of Occupancy-Reactive Space Heating Control

Field Study on Actual Usage of Occupancy-Reactive Space Heating Control

Control-Oriented, Data-Driven Models of Thermal Dynamics

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

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