Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs

Hong, Tianzhen; Chen, Yixing; Bélafi, Zsófia Deme; D’Oca, Simona

doi:10.1007/s12273-017-0396-6

Cited by 102 publications

(38 citation statements)

References 42 publications

(47 reference statements)

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“…As a simplification of general buildings, the standardized schedules might not be suitable for any specific building to be controlled. Additionally, the standardized schedules could not reflect the stochastic, diversified and dynamic behavior of occupant patterns, which is often the case in reality [24]. Due to the above limitations, methods to predict miscellaneous electric loads (MELs), lighting and occupants have been proposed, though no existing literatures discussing the prediction of internal heat gains as a whole have been found.…”

Section: Current State Of Internal Heat Gains Predictionmentioning

confidence: 99%

Data fusion in predicting internal heat gains for office buildings through a deep learning approach

2019

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Heating, Ventilation, and Air Conditioning (HVAC) is a major energy consumer in buildings. The predictive control has demonstrated a potential to reduce HVAC energy use. To facilitate predictive HVAC control, internal heat gains prediction is required. In this study, we applied Long Short-Term Memory Networks, a special form of deep neural network, to predict miscellaneous electric loads, lighting loads, occupant counts and internal heat gains in two United States office buildings. Compared with the predetermined schedules used in American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 90.1, the Long Short-Term Memory Networks method could reduce the prediction errors of internal heat gains from 12% to 8% in Building A, and from 26% to 16% in Building B. It was also found that for internal heat gains prediction, miscellaneous electric loads is a more important feature than occupant counts for two reasons. First, miscellaneous electric loads is the best proxy variable for internal heat gains, as it is the major component of and has the highest correlation coefficient with the internal heat gains. Second, miscellaneous electric loads contain valuable information to predict occupant count, while occupant count could not help improve miscellaneous electric loads prediction. These findings could help researchers and practitioners select the most relevant features to more accurately predict internal heat gains for the implementation of predictive HVAC control in buildings.

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Section: Current State Of Internal Heat Gains Predictionmentioning

confidence: 99%

Data fusion in predicting internal heat gains for office buildings through a deep learning approach

2019

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“…Occupant activity models link presence and activities of occupants and can consider the use of appliances, lighting or water related to these activities [28,29]. Recently, extensive reviews about existing models, the current state-of-the-art research and future challenges for occupant behavior modeling on the building-scale have been published [30,31,32,33,34].…”

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

159

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Urban building energy models aspire to become key planning tools for the holistic optimization of buildings, urban design, and energy systems in neighborhoods and districts. The energy demand of buildings is largely influenced by the behavior of the occupants. The insufficient consideration of occupant behavior is one of the causes to the "performance gap" in buildings -the difference between the simulated and the actually observed energy consumption. On the urban-scale impacts of different occupant behavior modeling approaches onto the various purposes of urban building energy models are still largely unknown. Research shows that the inappropriate choice of occupant behavior model could result in oversized district energy systems, leading to over-investment and low operational efficiency. This work therefore reviews urban building energy models in terms of their occupant behavior modeling approaches. Three categories of approaches are established and discussed: (1) deterministic space-based approaches, (2) stochastic space-based approaches, and (3) stochastic person-based approaches. They are further assessed in terms of their strategy to consider diversity in occupant behavior. Stochastic models, especially stochastic person-based models, seem to be superior to deterministic models. However, there are no stochastic models available yet that can be used for case studies of mixed-districts, comprising buildings of various occupancy types. In the reviewed urban-scale approaches, only single-use residential or office districts are modeled with stochastic techniques. However, people interact with various buildings on a daily basis. Their activities relate to their presence in different spaces at the urban-scale and to their use of appliances in those spaces. Their individual levels of comfort and behavioral patterns govern the control actions towards building systems. Therefore, a novel activity-based multi-agent approach for urban occupant behavior modeling is proposed as alternative to current approaches.Keywords: urban building energy model, energy-related occupant behavior, occupancy, occupant behavior model IntroductionUrban areas house more than half of the global population and cities are responsible for more than 70% of the global carbon dioxide emissions. At the same time, cities are crucial actors in the transition towards sustainable energy Email address: happle@arch.ethz.ch (Gabriel Happle)

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“…The concept of human behavior as an influence is similar to the previously-discussed schedules; however, there is a more stochastic element to this behavior. Buildings that are more influenced by occupant behavior generally have demand response-based control systems that use sensors, cameras or other detection methods to modulate systems only when humans are present or using the space for a specific purpose [29]. Sometimes humans even can control spaces using various types of interfaces with the building, although this is less common in non-residential buildings.…”

Section: Human Behaviormentioning

confidence: 99%

More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data

Miller

2019

MAKE

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Prediction is a common machine learning (ML) technique used on building energy consumption data. This process is valuable for anomaly detection, load profile-based building control and measurement and verification procedures. Hundreds of building energy prediction techniques have been developed over the last three decades, yet there is still no consensus on which techniques are the most effective for various building types. In addition, many of the techniques developed are not publicly available to the general research community. This paper outlines a library of open-source regression techniques from the Scikit-Learn Python library and describes the process of applying them to open hourly electrical meter data from 482 non-residential buildings from the Building Data Genome Project. The results illustrate that there are several techniques, notably decision tree-based models, that perform well on two-thirds of the total cohort of buildings. However, over one-third of the buildings, specifically primary schools, performed poorly. This example implementation shows that there is no one size-fits-all modeling solution and that various types of temporal behavior are difficult to capture using machine learning. An analysis of the generalizability of the models tested motivates the need for the application of future techniques to a board range of building types and behaviors. The importance of this type of scalability analysis is discussed in the context of the growth of energy meter and other Internet-of-Things (IoT) data streams in the built environment. This framework is designed to be an example baseline implementation for other building energy data prediction methods as applied to a larger population of buildings. For reproducibility, the entire code base and data sets are found on Github.

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Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs

Cited by 102 publications

References 42 publications

Data fusion in predicting internal heat gains for office buildings through a deep learning approach

Data fusion in predicting internal heat gains for office buildings through a deep learning approach

A review on occupant behavior in urban building energy models

More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data

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