Revealing occupancy patterns in an office building through the use of occupancy sensor data

Revealing occupancy patterns in an office building through the use of occupancy sensor data 1 Introduction</h5> 1.1 Background</h5> Energy simulation is an effective tool that helps building owners estimate and evaluate energy consumption for their buildings prior to construction or a major renovation. Energy simulation software can produce hourly energy consumption data by end use (lighting, heating, cooling, plug loads, etc.) for an entire year. In order to estimate performance of an actual building, models require inputs such as building size, shape, orientation, construction materials, heating, ventilation, and air-conditioning (HVAC) system size and type, interior and exterior lighting, and other physical parameters [1] . These physical inputs are relatively easy to find and can often be extracted from architectural and engineering plans. On the other hand, there are inputs that vary with time such as plug loads, lighting, blind position, ventilation rates, and heating and cooling loads [2] . Many of these inputs vary with time because they are occupancy related, others are weather dependent, and some are influenced by both. For example, lighting and plug loads, and in some applications fresh air requirements, greatly depend on the level of occupancy within a building [3] . That is, people usually operate electric lights, computers, and other common office devices when in a http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Energy and Buildings Elsevier

Revealing occupancy patterns in an office building through the use of occupancy sensor data

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Publisher
Elsevier
Copyright
Copyright © 2013 Elsevier B.V.
ISSN
0378-7788
eISSN
1872-6178
DOI
10.1016/j.enbuild.2013.08.062
Publisher site
See Article on Publisher Site

Abstract

1 Introduction</h5> 1.1 Background</h5> Energy simulation is an effective tool that helps building owners estimate and evaluate energy consumption for their buildings prior to construction or a major renovation. Energy simulation software can produce hourly energy consumption data by end use (lighting, heating, cooling, plug loads, etc.) for an entire year. In order to estimate performance of an actual building, models require inputs such as building size, shape, orientation, construction materials, heating, ventilation, and air-conditioning (HVAC) system size and type, interior and exterior lighting, and other physical parameters [1] . These physical inputs are relatively easy to find and can often be extracted from architectural and engineering plans. On the other hand, there are inputs that vary with time such as plug loads, lighting, blind position, ventilation rates, and heating and cooling loads [2] . Many of these inputs vary with time because they are occupancy related, others are weather dependent, and some are influenced by both. For example, lighting and plug loads, and in some applications fresh air requirements, greatly depend on the level of occupancy within a building [3] . That is, people usually operate electric lights, computers, and other common office devices when in a

Journal

Energy and BuildingsElsevier

Published: Dec 1, 2013

References

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