Correlation between occupants and energy consumption

Ahn, Ki-Uhn; Park, Cheol-Soo

doi:10.1016/j.enbuild.2016.01.010

Cited by 63 publications

(27 citation statements)

References 25 publications

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“…These approaches offer a powerful technique to describe the full effects of occupancy behaviors from large-scale real energy consumption data. They utilize methods from the fields of machine learning, pattern recognition (e.g., clustering), statistics, databases and visualization, which could provide reliable information on OB patterns, schedules and distribution [128,129]. With big data collection method becoming popular and the spread of 4G and 5G technology, OB collection from ICT (intelligent consumer technology) and intelligent sensors achieved large database, bringing valuable opportunities for understanding OB in the areas of: a) occupant movement and presence, b) thermal comfort; c) operation of windows, shades and blinds; and d) usage of lighting and electrical equipment [117].…”

Section: Big Data Collection From Ict and 5g Technologymentioning

confidence: 99%

A systematic review of occupant behavior in building energy policy

Yan

Azar

et al. 2020

Building and Environment

133

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Buildings play a dominant role in global efforts towards energy consumption reduction, greenhouse gas (GHG) emission mitigation, as well as global clean energy transition. Building Energy Policies (BEP) improved globally and quickly with a growing number of building codes implemented over the past decade. Occupant Behavior (OB) has significant impacts on building energy performance and occupant comfort, despite often being not well understood and oversimplified in BEPs. This paper highlighted the research needs of properly integrating OB in building energy polices by presenting a literature review to identify the key questions and challenges related to building technical standards and regulations, building information policies, building energy incentives, and policy evaluations and way forward. Challenges and opportunities of OB in BEP are also discussed with respect to technical innovation and digitalization, as well as concerns related to energy efficiency and fairness. There has been growing interests, research and applications in this field, but significant challenges and opportunities still lie ahead. Keywords: Occupant behavior; building energy policy; climate change policy; building energy efficiency;HVAC system 1. Introduction Occupant behavior in building energy policyBuildings play a dominant role in global efforts towards energy consumption reduction, greenhouse gas (GHG) emission mitigation, as well as global clean energy transition. Facing global climate change and national energy security challenges, international agreements, initiatives, and building sector energy policies are providing 2 promising alternatives to the status quo. In recent years, Building Energy Policies (BEP) improved globally and quickly with a growing number of building codes implemented over the past decade. In contrast, global final energy consumption in buildings increased by more than 5% between 2010 and 2017, as energy efficiency gains were outpaced by continued growth in the building sector size and resulting energy service demands [1]. Among various factors, Occupant Behavior (OB) plays an essential role in building energy demand increases and activity growth. In addition, OB has significant impacts on building energy performance and occupant comfort, despite often being not well understood and oversimplified in BEPs due to its stochastic, diverse, complex and interdisciplinary nature [2]. Definition, estimation and intervention of OB become essential research challenges during building energy policy making, implementation and evaluation processes. Real OB US OB China OB EU OB EU regulation China regulation OB in regulation US regulation Country X OB Country X regulation Gap 2 Gap 1 Gap 3

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Section: Big Data Collection From Ict and 5g Technologymentioning

confidence: 99%

A systematic review of occupant behavior in building energy policy

Yan

Azar

et al. 2020

Building and Environment

133

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“…(2) To facilitate occupancy-driven demand HVAC control modes: some researchers have argued that the supply air volume can be controlled according to occupancy information in each room. Instead of being based on the maximum design occupancy, the air volume should be dynamically controlled to meet demand for the detected occupancy requirement [57,58]. Once occupancy distribution is detected, it could benefit demand-based control modes by the occupancy-driven operation.…”

Section: Implications and Limitationsmentioning

confidence: 99%

Modeling occupancy distribution in large spaces with multi-feature classification algorithm

Wang

Chen

Hong

2018

Building and Environment

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Occupancy information enables robust and flexible control of heating, ventilation, and airconditioning (HVAC) systems in buildings. In large-sized spaces, multiple HVAC terminals are usually installed to provide cooperative services for different thermal zones. Occupancy information for large spaces, such as people counts, determines the cooperation among terminals. However, a people count at room-level is not adequate to optimize HVAC system operation due to occupant movement within the room leading to uneven distribution of loads. Without an accurate knowledge of occupants' spatial distribution, uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolution occupancy distribution is often perceived as a bottleneck for future improvement in HVAC operation efficiency. To fill this gap, this study proposes a multi-feature based k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable and low-cost Bluetooth Low Energy (BLE) networks. To demonstrate the proposed methods, an on-site experiment was conducted in a typical office of an institutional building. To validate the detection accuracy, the experiment outcomes were examined in three case studies, and one method based on City Block Distance (CBD) is used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. The results show that the accuracy of CBD = 1 is over 71.4% and accuracy of CBD = 2 can reach 92.9%.

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“…This may be close to a real-life situation in buildings (e.g., open/close windows, turn on/off lights/laptops) [20,24,25].…”

Section: #3mentioning

confidence: 99%

“…The random walk is a mathematical formalization of a path that consists of a succession of random steps (Equation (2)) [20,21]. The process disturbance (q L , Equation (4)) is defined as follows: Interestingly, the aggregated process disturbance (Equation (4)) depends on the characteristics of occupant behavior [20][21][22].…”

Section: Random Walk Hypothesis For the Process Disturbancementioning

confidence: 99%

Application of Kalman Filter for Estimating a Process Disturbance in a Building Space

Kim

Park

2017

Sustainability

Self Cite

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This paper addresses an application of the Kalman filter for estimating a time-varying process disturbance in a building space. The process disturbance means a synthetic composite of heat gains and losses caused by internal heat sources e.g., people, lights, equipment), and airflows. It is difficult to measure and quantify the internal heat sources and airflows due to their dynamic nature and time-lag impact on indoor environment. To address this issue, a Kalman filter estimation method was used in this study. The Kalman filtering is well suited for situations when state variables of interest cannot be measured. Based on virtual and real experiments conducted in this study, it was found that the Kalman filter can be used to estimate the time-varying process disturbance in a building space.

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Correlation between occupants and energy consumption

Cited by 63 publications

References 25 publications

A systematic review of occupant behavior in building energy policy

A systematic review of occupant behavior in building energy policy

Modeling occupancy distribution in large spaces with multi-feature classification algorithm

Application of Kalman Filter for Estimating a Process Disturbance in a Building Space

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