Monitoring Occupancy and Office Equipment Energy Consumption Using Real-Time Location System and Wireless Energy Meters

Buildings are one of the major energy consumers because of the need to meet occupants’ requirements. The commercial/institutional sector accounted for 14% of total energy consumption in Canada in 2009 while office buildings consumed 35% of this amount. Auxiliary equipment used 19% of the total energy consumed in office buildings. Previous studies showed the impact of occupancy behavior on IT equipment energy consumption. This thesis proposes a new method for monitoring occupancy behavior and energy consumption of IT equipment. Two wireless sensor technologies are investigated to collect the required data and to build an occupancy behavior estimation profile: Ultra-Wideband Real-Time Location System for occupancy location monitoring and ZigBee wireless energy meters for monitoring the energy consumption of IT equipment. The occupancy monitoring data gained from the UWB are used to create the occupants’ hourly profile. The occupancy profile based on short-time monitoring can be used to simulate long-term energy consumption. In conclusion, the comparison between the results shows up to 11% and 24% saving for heating loads and cooling loads, respectively. The proposed method profiles also resulted in up to 65% and 78% reduction for lighting and IT equipment energy consumption in the office, respectively. Therefore, dynamic occupancy driven profiles will reduce the energy consumption

Occupancy estimation in smart buildings using audio-processing techniques

In the past few years, several case studies have illustrated that the use of occupancy information in buildings leads to energy-efficient and low-cost HVAC operation. The widely presented techniques for occupancy estimation include temperature, humidity, CO2 concentration, image camera, motion sensor and passive infrared (PIR) sensor. So far little studies have been reported in literature to utilize audio and speech processing as indoor occupancy prediction technique. With rapid advances of audio and speech processing technologies, nowadays it is more feasible and attractive to integrate audio-based signal processing component into smart buildings. In this work, we propose to utilize audio processing techniques (i.e., speaker recognition and background audio energy estimation) to estimate room occupancy (i.e., the number of people inside a room). Theoretical analysis and simulation results demonstrate the accuracy and effectiveness of this proposed occupancy estimation technique. Based on the occupancy estimation, smart buildings will adjust the thermostat setups and HVAC operations, thus, achieving greater quality of service and drastic cost savings

Simulation of Local Climate Control in Shared Offices Based on Occupants Locations and Preferences

It is estimated that building energy consumption (BEC) accounts for one-third of the total global energy consumption, and Heating, Cooling, and Air-conditioning (HVAC) accounts for almost half of the energy consumption of buildings. To efficiently achieve more energy saving from the HVAC systems, narrowing the gap between the actual energy consumed and the demanded heating and cooling loads is found to be a promising strategy. Therefore, occupancy-driven HVAC management is attracting great attention. On the other hand, future smart buildings will have the ability to detect and locate the occupants, and adjust the HVAC system accordingly, which is expected to result in considerable energy savings. This research proposes a local climate control strategy in open space, such as shared offices, by dividing the space into zones according to the number of HVAC terminal units and adjusting the operation of each terminal unit based on occupants’ preferences and presence in the zone. To evaluate the performance regarding energy consumption and occupancy thermal comfort, and the feasibility of the proposed local climate control, three case studies are implemented. The occupancy presence pattern is captured by a Bluetooth Low Energy (BLE)-based tracking system. Based on a four-week test carried out in a graduate laboratory in Concordia University, the occupancy profiles and different HVAC operation scenarios are created as the inputs of the building energy simulation. The simulation is run for three months for cooling and the results show that, with the adoption of the proposed local climate control strategy, 15% or 36% of the energy consumption can be saved compared with applying a dynamic schedule using a motion detector or a fixed schedule, respectively. In addition, the occupants’ comfort level can be increased by an average of 6%. In addition, sensitivity analysis is conducted with respect to the factors affecting the effectiveness of the proposed climate control strategy and the HVAC setpoint temperature. It is concluded that the proposed local climate control strategy is effective in reducing the energy consumption and improving occupancy thermal comfort, however, the extent of the effectiveness depends on factors of building properties, occupancy attributes, and HVAC operation

The Impact of Occupants’ Behaviours on Energy Consumption in Multi-Functional Spaces

Over the last 15 years, the estimation of energy consumption in buildings has become a critical process during various stages of building’s lifecycle due to growing global scientific and political pressure to respond to climate change. It has been widely acknowledged in the literature that there is a distinct performance gap between predicted and actual energy consumption of buildings which has attracted scholars across the world to investigate the sufficiency of software inputs and presumptions regarding how the buildings are actually used. Several studies have confirmed that occupant’s presence, in addition to, their interactions with building systems (such as: opening door and window, changing the thermostat set-point and using appliances), known as passive and active energy consumption behaviours, play significant roles in building’s energy consumption. However, the incorporation of occupants’ behaviours into the building energy performance analysis has been mostly overlooked. Most of the existing studies on the impacts of occupants on building energy consumption have focused on residential and office buildings. Therefore, there is a lack of knowledge about the impacts of occupants’ behaviours on energy consumption in public buildings such as: galleries, exhibitions, recreational facilities and institutional buildings. In such building occupants have limited access to building systems, and their energy consumption behaviours are limited to their presence and the production of metabolic heat (passive behaviour), in addition to, few activities such as: opening the entrance door. This research develops a conceptual framework to improve the accuracy of energy consumption assessment in multi-functional spaces at different stages of building’s lifecycle by integrating the impacts of occupants’ behaviours into building energy predictions to reduce the gap between actual and predicted energy consumption. In this quantitative research, a model simulation method is applied on multiple cases at different stages of the building lifecycle including design, construction and post-occupancy. The first two cases are multi-functional spaces of public buildings at the design and construction stages, which were studied to address the missing information and potential gaps in energy modelling and simulation. The study was then taken forward using case studies at the post-occupancy stage to integrate the realistic observed data into the building energy simulation tool. For each of the cases, energy simulation was run twice: first, using default values of the software, and second, using the collected data. The data collection included hourly observation of 38 zones in both cases at the post-occupancy stage for the duration of two weeks, in addition to, using available governmental and real-time statistics. The analysis of energy simulation results using default software values and collected data highlighted that lack of sufficient information regarding building working hours, space layout and function, occupancy density and schedules, the entrance door opening time and HVAC set-points may result significant performance gaps in energy consumption prediction of multi-functional spaces in institutional buildings and galleries. This study provides conceptual frameworks for the prospect energy modellers and researchers to obtain more accurate energy consumption predictions for multi-functional spaces of public buildings