Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

Commercial buildings contribute to 19% of the primary energy consumption in the US, with HVAC systems accounting for 39.6% of this usage. To reduce HVAC energy use, prior studies have proposed using wireless occupancy sensors or even cameras for occupancy based actuation showing energy savings of up to 42%. However, most of these solutions require these sensors and the associated network to be designed, deployed, tested and maintained within existing buildings which is significantly costly.We present Sentinel, a system that leverages existing WiFi infrastructure in commercial buildings along with smartphones with WiFi connectivity carried by building occupants to provide fine-grained occupancy based HVAC actuation. We have implemented Sentinel on top of RESTful web services, and demonstrate that it is scalable and compatible with legacy building management. We show that Sentinel accurately determines the occupancy in office spaces 86% of the time, with 6.2% false negative errors. We high-light the reasons for the inaccuracies, mostly attributed to aggressive power management by smartphones. Finally, we actuate 23% of the HVAC zones within a commercial building using Sentinel for one day and measure HVAC electrical energy savings of 17.8%.

/pdf/sentinel-occupancy-based-hvac-actuation-using-existing-wifi-ijblyya0iv.pdf

Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings

https://orca.cf.ac.uk/88855/1/Metering_final-version.pdf

Building energy metering and environmental monitoring – A state-of-the-art review and directions for future research

https://pure.tue.nl/ws/files/13467037/1_s2.0_S0378778815001334_main.pdf

Occupancy measurement in commercial office buildings for demand-driven control applications : a survey and detection system evaluation

This paper reviews the discrepancy between predicted and measured energy use in non-domestic buildings in a UK context with outlook to global studies. It explains differences between energy performance quantification and classifies this energy performance gap as a difference between compliance or performance modelling with measured energy use. Literary sources are reviewed in order to signify the magnitude between predicted and measured energy use, which is found to deviate by +34% with a standard deviation of 55% based on 62 buildings. It proceeds in describing the underlying causes for the performance gap, existent in all stages of the building life cycle, and identifies the dominant factors to be related to specification uncertainty in modelling, occupant behaviour and poor operational practices having an estimated effect of 20-60%, 10-80% and 15-80% on energy use respectively. Other factors that have a high impact are related to establishing the energy performance target, impact of early design decisions, heuristic uncertainty in modelling and occupant behaviour. Finally action measures and feedback processes in order to reduce the performance gap are discussed, indicating the need for energy in-use legislation, insight into design stage models, accessible energy data and expansion of research efforts towards building performance in-use in relation to predicted performance

/pdf/a-review-of-the-regulatory-energy-performance-gap-and-its-43kg2sk22r.pdf

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

ENERNET: Studying the dynamic relationship between building occupancy and energy consumption

Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset. Results demonstrated that high-resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain.

Prudence Robinson

Papers

ENERNET: Studying the dynamic relationship between building occupancy and energy consumption

Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model