Improving energy efficiency in office buildings through adaptive control of the indoor climate

Abstract

Zone level heating and cooling systems, lighting and blinds in offices have been either controlled by occupants or automated based on fixed setpoints and schedules. Occupants' control of these systems target maintaining a comfortable indoor climate with minimum number of control actions with little intention to save energy. This leads to the suboptimal utilization of daylight and passive solar heat gains. However, when they are automated to mitigate the extravagance in occupant behaviour, operators tend to choose conservative setpoints and schedules that maintain the comfort of the majority and minimize the frequency of complaints with little consideration to save energy again. Although we have been treating the building controllers as our servants maintaining fixed setpoints and schedules, they represent great potential to implement distributed artificial intelligence. To this end, within the scope of this thesis, an adaptive indoor climate control tool was developed. The tool contains novel algorithms that recursively learn from the occupancy patterns, adaptive occupant behaviours, and develop an inverse model of the heat transfer problem in each zone. The algorithms were designed to be embedded inside local building controllers to undertake the learning process in real-time using a small number of low-cost building sensors. The information derived from the occupants and the building's temperature response was used to autonomously choose operating setpoints and schedules-tailored to exploit the nuances amongst subspaces in a building. The algorithms were implemented and tested for over a year in a controls laboratory which was a shared-office space with a standalone controls network. Furthermore, iii they were implemented and tested in private offices for over a year. Energy and daylighting simulations were conducted to analyze alternative scenarios regarding the design of these algorithms. Implementation results indicate that the use of adaptive indoor climate control algorithms developed in this thesis can substantially reduce the space heating, cooling, and lighting loads in office buildings-without adversely affecting the occupant comfort. The simulation results indicate that adaptive control of the indoor climate not only reduces the expected load intensities but also reduces the risk of poor operational decisions leading to discomfort and excessive energy use. iv Hofstadter 's Law: It always takes longer than you expect, even when you take into account Hofstadter 's Law.

Characterization of a building's operation using automation data: A review and case study

“…The faults can be grouped as hard-faultsissues in sensors, actuators, equipmentor soft-faultsissues in controls programming [19]. Hard-faults often require component replacement and manual-labour, whereas most soft-faults can be automatically corrected without human intervention (e.g., [20,21]). The process of detecting and isolating faults in tandem is defined as fault diagnosis.…”

“…Only a small fraction of the studies focus on faults in the VAV terminal units and soft-faults in the VAV-AHU systems. In reality, an AHU serves many VAV thermal zones; and thus, in modern buildings, more than 90% of the sensors and actuators are distributed to individual zones [20,23,24]. This situation represents great potential for us to optimize energy use and comfort by providing indoor climates tailo ed fo ea h zo e's o upa a d o fo t p efe e es [20].…”

“…In reality, an AHU serves many VAV thermal zones; and thus, in modern buildings, more than 90% of the sensors and actuators are distributed to individual zones [20,23,24]. This situation represents great potential for us to optimize energy use and comfort by providing indoor climates tailo ed fo ea h zo e's o upa a d o fo t p efe e es [20]. However, distributed sensing also represents a major challenge; as more sensors and actuators mean a larger number of components and equipment that can fail, and need to be diagnosed and maintained.…”

“…Figure 12 and Figure 13 present the estimated parameter values of the inverse greybox perimeter and core zone models, respectively. By looking at the parameter x 1 (the link between indoor and outdoor temperature difference on indoor temperature), three thermal zones (20,21,23) appeared to be a good candidate for an envelope retrofit (e.g., sealing drafty exterior windows and doors). In two thermal zones (20,21), by looking at the parameter x 2 , the solar gains appeared to play a substantial role in their temperature response.…”

“…By looking at the parameter x 1 (the link between indoor and outdoor temperature difference on indoor temperature), three thermal zones (20,21,23) appeared to be a good candidate for an envelope retrofit (e.g., sealing drafty exterior windows and doors). In two thermal zones (20,21), by looking at the parameter x 2 , the solar gains appeared to play a substantial role in their temperature response. Relatively high x 3 values in Zones 3, 8, 15, 20 and 22 can be interpreted as these zones are exposed to high plug-in equipment loads; whereas relatively low x 3 value for Zone 26 can be interpreted as an indication of an unoccupied space.…”