Problem
Heating, Ventilation, and Air Conditioning (HVAC) accounts for about 40% of the energy consumption in buildings. By changing the indoor air temperature of a building to be closer to the outdoor air temperature—for example, maintaining the building at a warmer temperature during summer months—HVAC energy consumption can be reduced by 10-40%. However, this comes at the cost of a reduction in individual comfort.

Solution
We have designed and implemented SPOT: a Smart Personalized Office Thermal control system. A SPOT device is placed in individual office spaces to heat or cool the immediate area to a comfortable temperature when an occupant is present. This allows the temperature of a building to be set to a value lower than normal in winter and to a value higher than normal in summer.

The first version of SPOT used 6 parameters to predict personal comfort: air temperature, radiant temperature, humidity, air speed, clothing level, and activity level. We made three iterations on SPOT’s design to improve its balance between energy conservation and personal thermal comfort, as described next.

SPOT
The first iteration of SPOT uses a Microsoft Kinect and a variety of sensors to measure the six parameters mentioned above. SPOT is able to calculate the amount of clothing a person is wearing using data from the infrared sensor and the Kinect. Once a user enters a room, SPOT measures these parameters, then controls a radiant heater to heat the workspace to a comfortable temperature. More details can be found here:

P. X. Gao. SPOT: A Smart Personalized Office Thermal Control System,  MMath thesis, University of Waterloo, May 2013.
P. X. Gao and S. Keshav. SPOT: A Smart Personalized Office Thermal Control System, Proc. ACM e-Energy, May 2013.

SPOT+ (SPOT Plus)
SPOT+ improves upon SPOT by performing predictive control rather than reactive control. That is, SPOT+ will begin heating a workspace 10 minutes before a user walks in, so when they arrive the workspace is already at a comfortable temperature. It will also predict when a user will leave, so that it can begin cooling earlier to save energy.

After deploying SPOT+, we found that it reduced energy usage by 60% compared to a fixed temperature setting, and it reduces personal thermal discomfort from 0.36 to 0.02 (in the ASHRAE comfort scale) compared to SPOT.

P.X. Gao and S. Keshav. Optimal Personal Comfort Management Using SPOT+, Proc. BuildSys Workshop,  November 2013. (Winner of the Best Student Paper Award.)

SPOT* (SPOT Star)
Our third version improves on SPOT and SPOT+ in 5 distinct ways:

• It provides both heating and cooling, unlike the previous iterations which only provided heating.
• It uses a speed-controlled desktop fan instead of a radiant-heater, making it possible to rapidly change the room temperature in response to discomfort.
• It is far less intrusive than the prior systems, because it does not use a camera.
• SPOT* is about an order of magnitude less expensive – while SPOT/SPOT+ were $1000 a unit, this prototype model costs only $185 a unit, with costs dropping to below $100 in mass production.
• Flexible placement of its software components allow for a balance between cost, privacy, and data durability.

In our deployment, we found that SPOT* improved user comfort by 78% compared to traditional HVAC systems.

Rabbani and S. Keshav, The SPOT* System for Flexible Personal Heating and Cooling, Poster, Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems (e-Energy), July 2015, 209-210. Best Poster Award
Rabbani and S. Keshav, “The SPOT* Personal Thermal Comfort System,” Proc. ACM BuildSys’16, November 2016.

Controlling an HVAC system with SPOTs deployed
To integrate SPOT systems into everyday use, we explore how to make HVAC systems SPOT-aware. We propose a control strategy to update the temperature set points for an HVAC system using the following factors: occupancy status in each room, preferred comfort requirements of occupied rooms, zones which have SPOT systems in it, outside temperature, and thermal properties of each room.

In a simulation setting, when users have homogeneous comfort requirements, we find that our system provides 45% savings in energy during the summer, and 15% during the winter compared to current predictive HVAC systems. When users have heterogeneous comfort requirements, our system provides 50% improvement in comfort in the summer and about 30% in winter, on top of significant energy savings.

Kalaimani, M. Jain, S. Keshav, and C. Rosenberg, ”On the Interaction between Personal Comfort Systems and Centralized HVAC Systems in Office Buildings, ” J. Advances in Building Energy Research, August 2018, V7:p.1-29.

Mitigating the impact of occupancy prediction errors in HVAC performance
Many commercial buildings use model predictive control (MPC) to control their HVAC systems – the model predicts outside air temperature and the number of people that will be in each zone of a building on a given day and adjusts the HVAC system accordingly. A prediction model cannot be perfect however – when the prediction errors of a model increase from 5% to 20%, the performance of the HVAC controller, as measured by occupant comfort and building energy use, becomes worse than that of a simple static scheduler that changes the temperature setpoint at the beginning and the end of the day.

We found that by employing the SPOT Aware strategy for HVAC systems, we stay in the acceptable region of occupancy comfort 95% of the time as opposed to only 83% when there are prediction errors in the MPC system. Thus, installing SPOT systems can not only save energy, but also make building occupants more comfortable, even in the presence of forecasting errors.

M. Jain, R. Kalaimani, S. Keshav, and C. Rosenberg, ”Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance,” Energy Informatics, 2018 1:60, https://doi.org/10.1186/s42162-018-0064-9, December 2018.