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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.
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.
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:
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.
A significant barrier to the adoption of e-bikes is range anxiety, or the fear of running out of battery with no place to recharge. Currently, e-bikes do not display the estimated range available. Instead, a digital display shows battery voltage, but it is difficult to estimate the remaining range of a bike based on this value. Though e-bike manufacturers do publish the maximum range of their models, we found that this number is not an accurate predictor for all riders, depending on how aggressively they ride. Note that any additional hardware required for range prediction must be inexpensive, in order to keep overall prices low.
Using data from a fleet of 31 sensor-equipped e-bikes used in the University of Waterloo WeBike project, combined with OpenStreetMap data, we evaluate two range prediction models for e-bikes. The first model is a simple one, based on the average battery consumption from past trips. The second model is a more complex linear regression model that considers the characteristics of the anticipated route (such as off-road percentage, the number of stop signs, and the number of traffic lights), as well as battery temperature.
We found that the more complex linear regression model didn’t perform much better than the simpler one. Using real trip data, our predictions using the simple model were usually within a 10% of the actual remaining range at the end of the trip.
These results should be of interest to e-bike manufacturers because a simple on-board prediction technique can be implemented by measuring battery voltage, battery current, and mileage. Since most e-bikes have an odometer built in, by making this odometer data accessible and deploying additional sensors at the battery, our technique can be implemented inexpensively.
L. Gebhard, L. Golab. S. Keshav, and H. de Meer, “Range prediction for electric bicycles,” Proc. ACM e-Energy 2016.
Electric vehicles (EVs) pose a challenge to the electrical grid in two ways.
Since a typical EV charger is located within 3km of the nearest substation, the transmission delay between any charger and its connected substation is less than 1ms. As such, we can design a distributed control algorithm that adjusts the charging rate of an EV every few milliseconds, in response to the load being placed on the overall distribution system. For example, if an EV is charging at a rate that affects the reliability of the grid, its charging rate can be decreased.
Three papers were written on this subject. The first paper introduces the problem and describes how the congestion control problem for a grid distribution system is similar to the congestion control problem in the Internet.
By using a mathematical framework originally developed for rate control in the Internet (TCP), each EV charger in the grid can independently update its charging rate, while ensuring that the overall load on the grid stays at an ideal level, the allocated rates for each charger are proportionally fair, and that these allocations are optimal. The second paper in this series focuses on a static network scenario, in which the non-EV load is constant, and a fixed number of EVs are connected to chargers.
The third paper goes into detail about the dynamic network scenario, which involves variable home loads and number of plugged-in EVs. Since the dynamic network scenario can be decomposed into a series of static intervals, the static control algorithm described above can be extended to be used in a dynamic network.
We show that in a test setting, only 70 EVs could be fully charged without control, whereas up to around 700 EVs can be fully charged using our control algorithm. This work was further extended to integrate EV charging control with control of distributed storage, while accounting for distributed solar generation. Details can be found here: O. Ardakanian, S. Keshav, C. Rosenberg, “Integration of Renewable Generation and Elastic Loads into Distribution Grids,” Springer, 2016.
E‐bikes are revolutionizing transportation in China and parts of Europe, yet little is known about user patterns in North America, particularly Canadian cities where uptake tends to lag. To fill this gap in knowledge, the University of Waterloo studied a sample of e-bike riders over three years in Kitchener-Waterloo, Ontario, Canada. The field trial, called WeBike, amassed over 150GB of data on e-bike usage by faculty, staff, and students from the summer of 2014 until spring of 2017.
Three papers were published by the University of Waterloo analyzing this data – the first two are quantitative in nature, and the last one is qualitative.
Quantitative data collected from the e-bikes included GPS, acceleration, and battery charge and discharge data. Based on our analysis, we draw several conclusions:
I. Rios, L. Golab. and S. Keshav, “Analyzing the Usage Patterns of Electric Bicycles,” EV-SYS Workshop at ACM e-Energy, 2016.
C. Gorenflo, I. Rios, L. Golab, S. Keshav, “Usage Patterns of Electric Bicycles: An Analysis of the WeBike Project,” Journal of Advanced Transportation, October 2017.
Some notable findings in the qualitative study include:
S. Edge, J. Dean, M. Cuomo, and S. Keshav, “Exploring e-bikes as a mode of sustainable transport: a temporal qualitative study of the perspectives of a sample of novice riders in a Canadian city“, Canadian Geographer/Le Geographe Canadien, April 2018.
As grid-provided electricity continues to rise in cost, individuals, small companies, and building operators are increasingly looking to offset grid usage using solar photovoltaic (PV) panels and energy storage. When constrained by a budget however, it is difficult to predict the optimal ratio of solar PV panels and storage that one should buy to completely or partially offset grid usage.
We tested three ways to determine the possible sizing(*) required to meet the anticipated load in an off-grid system. Out of the three methods–simulation, optimization, and stochastic network calculus–we found that the easiest method, simulation, also provided the best results.
(*) sizing: the power or energy size of the storage in kW/kWh, and the size of solar generation in kWp.
We are currently building a web application to help predict the best sizing for a solar energy system. The tool will be linked here when completed.
F. Kazhamiaka, S. Keshav, and C. Rosenberg, Robust and Practical Approaches for Solar PV and Storage Sizing, Proc. ACM eEnergy 2018, June 2018.
The best-known lithium-ion battery model whose parameters can be calibrated entirely from a battery’s manufacturer-provided specifications (ie. internal resistance, nominal capacity, voltage at full charge, etc) was proposed by Tremblay et al and is widely use. However, it has some shortcomings. For instance, it has low fidelity at high charge and discharge rates. Moreover, it only models a cell, and does not model the battery management system. This makes it unsuitable for evaluating practical storage systems.
We propose an alternative, the Power-based Integrated (PI) model, whose parameters can also be calibrated entirely from manufacturer specifications, but has much higher fidelity across a wide range of charge/discharge rates. This model is freely available in the public domain as a Matlab system block compatible with Simulink simulation software. Access the block here.
We suggest using our model when:
F. Kazhamiaka, S. Keshav, C. Rosenberg, and K.-H. Pettinger, ”Simple Spec-Based Modelling of Lithium-Ion Batteries, ” IEEE Transactions on Energy Conversion, Vol 33, No. 4, December 2018.