Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use

Tang, Rui; Wang, Shengwei; Sun, Shaobo

doi:10.1007/s12273-020-0605-6

Cited by 41 publications

(18 citation statements)

References 26 publications

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“…This is another important factor to be considered in model calibration. Besides, occupant behaviors impact the energy efficiencies in buildings significantly (Tang, Wang, and Sun 2021;Zhou et al 2022) and should also be considered in model calibration in future research.…”

Section: Discussionmentioning

confidence: 99%

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

et al. 2022

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Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

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Section: Discussionmentioning

confidence: 99%

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

et al. 2022

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“…over time, while occupant profiles indicate occupant thermal profile and occupant behavior (e.g., adjust setpoint, open door/window, turn on/off the electrical appliances, etc.) related to building energy performance (Tang et al 2021).…”

Section: Grouped Pointsmentioning

confidence: 99%

From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control

et al. 2021

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Occupants are the core of the built environment. Traditional heating, ventilation, and air-conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC “Occupant-centric Control (OCC).” HVAC OCC strategy starts with collecting the occupant’s information (e.g., presence/absence) and then applies it to meet the occupant’s requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. We provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.

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“…Building energy prediction requires a prior step before applying a strategic optimization, which has been a hot topic in recent studies. Many energy prediction models are data-driven (Tang et al 2020), taking advantage of machine learning algorithms and integrating data with physics knowledge in buildings to improve the accuracy of the prediction, thus offering reliable information for the next step of the control optimization (Zakula et al 2014). Specifically, for control optimization, most studies have used deep reinforcement learning to build a step-forward prediction and optimization mechanism under practical control scenarios (Dalamagkidis et al 2007;Lee and Braun 2008;Yuan et al 2020;Chen et al 2020).…”

Section: Energy-efficient-oriented System Control Optimizationmentioning

confidence: 99%

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

et al. 2020

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Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

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Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use

Cited by 41 publications

References 26 publications

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

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