User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings

Jazizadeh, Farrokh; Ghahramani, Ali; Becerik-Gerber, Burçin; Kichkaylo, Tatiana; Orosz, Michael

doi:10.1016/j.enbuild.2013.11.066

Cited by 184 publications

(84 citation statements)

References 28 publications

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“…In addition to simulating the design phase of the buildings, simulation tools could also be used during other phases such as the construction and operation phases [28,63,75,[80][81][82][83]. For instance, within the renovation phase of buildings, such tools could help decision makers choose the most efficient appliances/systems when making a purchase.…”

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

“…However, they validated the model by single offices, which is problematic since for such studies, more cases-especially multiple offices-need to be considered to study occupants' stochastic movements. Jazizadeh et al [82,83] developed a framework that models occupants' thermal preference profiles into HVAC control logic in order to set room conditions at occupants' desired temperatures. They employed a fuzzy based model to put occupants' comfort profiles into the framework.…”

Section: Other Techniquesmentioning

confidence: 99%

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A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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Abstract:Buildings currently account for 30-40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States' energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants' energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings' energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants' energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants' energy-consuming behaviors in order to assess and predict such characteristics' impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change OPEN ACCESSEnergies 2015, 8 10997 occupants' energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

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Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

Section: Other Techniquesmentioning

confidence: 99%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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“…In recent years, an increasing number of studies [17,[19][20][21][22][23][24][25] have attempted to develop different forms of personal comfort models in order to describe unique comfort characteristics of individual occupants based on the data collected from the actual spaces. These models predict individuals' thermal comfort by correlating environmental measurements with occupant feedback obtained via survey.…”

mentioning

confidence: 99%

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Kim

Zhou

Schiavon

et al. 2018

Building and Environment

339

162

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“…Finally, the proposed cost function is normalized by means of weighting parameters. (16) In the previous equation, the first term on the right side is the set-point tracking, i.e., the difference between the predicted output and the desired reference, whereas the second term is considered the control effort. N and N u represent the prediction and control horizons, respectively; Y PMV (k + j|k) is the predicted output (the PMV index) estimated at sample time k + j with the information available at time k by means of a discrete version of the continuous linear model given by Equation (15); PMV re f (k + j|k) represents the future PMV index reference; u(k + j − 1) is the future control signal, that is fan coil speed (V Fan ); δ(j) and λ(j) are the weights associated with set-point tracking and control effort, respectively.…”

Section: Economic Model-based Predictive Controllermentioning

confidence: 99%

“…The fuzzy controller is fed with these predictions in order to calculate a suitable control action. Another fuzzy logic controller is used in Jazizadeh et al [16], which uses building occupants' personalized thermal profiles in order to improve its efficiency. The results show a reduction in daily average airflow rates by 26% when the users' personalized thermal profiles are used instead of predefined ones.…”

Section: Introductionmentioning

confidence: 99%

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

et al. 2017

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Abstract:The goal of maintaining users' thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users' productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users' thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.

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User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings

Cited by 184 publications

References 28 publications

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

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