A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting

Dong, Bing; Lam, Khee Poh

doi:10.1007/s12273-013-0142-7

Cited by 224 publications

(131 citation statements)

References 16 publications

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“…Therefore, much research focuses on predicting occupancy patterns for HVAC control: AIM [29], and [30] in Europe and the UK; IntelligentLighting [59], IBMIntelligentBuilding [61], ACHE [33], the self-programming thermostat [35], the smart thermostat [36], OBSERVE [54], and the research of Bing Dong and his colleagues [77] are conducted in the U.S.…”

Section: Prediction Of Occupancy Patternsmentioning

confidence: 99%

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Energy intelligent buildings based on user activity: A survey

Nguyen

Aiello

2013

Energy and Buildings

597

322

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a b s t r a c tOccupant presence and behaviour in buildings has been shown to have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building controls. Energyunaware behaviour can add one-third to a building's designed energy performance. Consequently, user activity and behaviour is considered as a key element and has long been used for control of various devices such as artificial light, heating, ventilation, and air conditioning. However, how are user activity and behaviour taken into account? What are the most valuable activities or behaviours and what is their impact on energy saving potential? In order to answer these questions, we provide a novel survey of prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition. We devise new metrics to compare the existing studies. Through the survey, we determine the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads. The most promising and appropriate activity recognition technologies and approaches are discussed thus allowing us to conclude with principles and perspectives for energy intelligent buildings based on user activity.

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Section: Prediction Of Occupancy Patternsmentioning

confidence: 99%

“…Bing Dong and his colleagues introduce and illustrate a method for integrated building heating, cooling and ventilation control to reduce energy consumption and maintain indoor temperature set points, based on the prediction of occupant behaviour patterns and local weather conditions in [60], and [77].…”

Section: Prediction Of Occupancy Patternsmentioning

confidence: 99%

Energy intelligent buildings based on user activity: A survey

Nguyen

Aiello

2013

Energy and Buildings

597

322

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“…obFMU functions as a solver for occupant behavior models that are represented in obXML.  Model predictive controls (MPC) have been developed combining building emulation model with occupant behavior models (via co-simulation or embedded Modelica code) that predict the likelihood and effect of occupancy and adaptive actions over time [60,113,114].…”

Section: 12supporting Research Advancementsmentioning

confidence: 99%

“…Innovative building EMCS and technologies are now available to support smart building automation and operation [59,138]. Based on machine learning processes, indoor and outdoor environmental parameters, as well as occupant presence, comfort, and action data are labeled, memorized, and re-employed to improve algorithms ruling the control system of the building automation system (BAS) [61,114].…”

Section: 32supporting Research Advancementsmentioning

confidence: 99%

The human dimensions of energy use in buildings: A review

D’Oca

Hong

Langevin

2018

Renewable and Sustainable Energy Reviews

290

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The "human dimensions" of energy use in buildings refer to the energy-related behaviors of key stakeholders that affect energy use over the building life cycle. Stakeholders include building designers, operators, managers, engineers, occupants, industry, vendors, and policymakers, who directly or indirectly influence the acts of designing, constructing, living, operating, managing, and regulating the built environments, from individual building up to the urban scale. Among factors driving high-performance buildings, human dimensions play a role that is as significant as that of technological advances. However, this factor is not well understood, and, as a result, human dimensions are often ignored or simplified by stakeholders. This paper presents a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area. The paper highlights research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity. This research focuses on identifying key needs for each stakeholder involved in a building's life cycle and takes an interdisciplinary focus that spans the fields of architecture and engineering design, sociology, data science, energy policy, codes, and standards to provide targeted insights.Greater understanding of the human dimensions of energy use has several potential benefits including reductions in operating cost for building owners; enhanced comfort conditions and productivity for building occupants; more effective building energy management and automation systems for building operators and energy managers; and the integration of more accurate control logic into the next generation of human-in-the-loop technologies. The review concludes by summarizing recommendations for policy makers and industry stakeholders for developing codes, standards, and technologies that can leverage the human dimensions of energy use to reliably predict and achieve energy use reductions in the residential and commercial buildings sectors.

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“…Additionally, Virote and Nueves-Silva [15] used the Markov Chain method to relate behavior in an office space catalogued by data logger measurements to occupant presence in the office building. More recently in 2014, Dong and Lam [16] developed a real-time predictive control model for building heating and cooling systems based upon the occupancy behavior pattern detection in coordination with local weather forecasting, using advanced machine learning methods including Adaptive Gaussian Process, Hidden Markov Model, Episode Discovery and Semi-Markov Model. Currently, more granular real-time measurements of the occupant presence, movement and interaction with system controls (thermostats, lighting) and building envelope action (windows, shades) are streamed.…”

Section: Introductionmentioning

confidence: 99%

Occupancy schedules learning process through a data mining framework

D’Oca

Hong

2015

Energy and Buildings

248

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Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. The identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.

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A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting

Cited by 224 publications

References 16 publications

Energy intelligent buildings based on user activity: A survey

Energy intelligent buildings based on user activity: A survey

The human dimensions of energy use in buildings: A review

Occupancy schedules learning process through a data mining framework

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