Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Occupancy Data Sensing, Collection, and Modeling for Residential Buildings
Residential buildings account for approximately half of the overall energy and electricity consumed by the building sector in the United States. Much of this energy and electricity consumed is a result of occupant behaviors and occupant-dependent energy-consuming devices and appliances. Residential buildings’ heating, ventilation, and air conditioning (HVAC) systems, which are designed to meet the comfort requirements of residents, are responsible for more than half of this energy consumption. Similarly, large appliances used by occupants, such as for cooking and water heating, also represent a substantial amount of residential consumption. The energy consumption of these systems depends significantly on the type and sizing of the equipment and/or appliances as well as how occupants utilize these systems and their preferred thermal comfort conditions. Beyond these, plug loads, lighting loads, and miscellaneous loads are also highly dependent on occupants’ activity patterns and their level of interaction with these energy-consuming systems. Therefore, since residential buildings’ energy consumption is directly associated with occupant activities, knowledge of occupant activities and load patterns in residential buildings can help to both quantify energy consumption patterns for energy modeling applications, variability in residential energy use, as well as be used for estimating the savings potential of various equipment, controls, and retrofits. Accordingly, this chapter reviews the current state of the art in residential occupancy and load modeling, including datasets and data challenges, and methods of occupancy and activity pattern modeling, as well as current research in the use of this information to better estimate the energy and/or demand savings potential of technologies and controls.
Occupancy Data Sensing, Collection, and Modeling for Residential Buildings
Residential buildings account for approximately half of the overall energy and electricity consumed by the building sector in the United States. Much of this energy and electricity consumed is a result of occupant behaviors and occupant-dependent energy-consuming devices and appliances. Residential buildings’ heating, ventilation, and air conditioning (HVAC) systems, which are designed to meet the comfort requirements of residents, are responsible for more than half of this energy consumption. Similarly, large appliances used by occupants, such as for cooking and water heating, also represent a substantial amount of residential consumption. The energy consumption of these systems depends significantly on the type and sizing of the equipment and/or appliances as well as how occupants utilize these systems and their preferred thermal comfort conditions. Beyond these, plug loads, lighting loads, and miscellaneous loads are also highly dependent on occupants’ activity patterns and their level of interaction with these energy-consuming systems. Therefore, since residential buildings’ energy consumption is directly associated with occupant activities, knowledge of occupant activities and load patterns in residential buildings can help to both quantify energy consumption patterns for energy modeling applications, variability in residential energy use, as well as be used for estimating the savings potential of various equipment, controls, and retrofits. Accordingly, this chapter reviews the current state of the art in residential occupancy and load modeling, including datasets and data challenges, and methods of occupancy and activity pattern modeling, as well as current research in the use of this information to better estimate the energy and/or demand savings potential of technologies and controls.
Occupancy Data Sensing, Collection, and Modeling for Residential Buildings
Green Energy,Technology
Sadat-Mohammadi, Milad (Herausgeber:in) / Nazari-Heris, Morteza (Herausgeber:in) / Asadi, Somayeh (Herausgeber:in) / Mohammadi-Ivatloo, Behnam (Herausgeber:in) / Jebelli, Houtan (Herausgeber:in) / Mitra, Debrudra (Autor:in) / Malekpour Koupaei, Diba (Autor:in) / Cetin, Kristen (Autor:in)
02.09.2022
19 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| BASE | 2016
A new modeling approach for short-term prediction of occupancy in residential buildings
| British Library Online Contents | 2017