Conference PaperPDF Available

The Weather Impact on Efficient Home Heating with Smart Thermostats

Authors:

Abstract and Figures

In this paper, we compare the currently implemented smart heating algorithms with our proposed method - Weather Based Heating Algorithm. Our simulations are based on the data from years 2015-2018, provided by Ecobee Inc., which is one of the major manufacturers of smart thermostats. The main problem that we focus on is the study of the overheated homes. Our results showed that the problem of overheated households during winter affects North America. The significant impact is visible during sunny days. On the other hand, the implementation of adaptive strategies, such as Weather Based Heating algorithm, results in three times reduction of overheated time and 20% reduction in energy costs.
Content may be subject to copyright.
The Weather Impact on Efficient Home Heating
with Smart Thermostats
Michal Aibin
British Columbia Institute of Technology, Department of Computing, Vancouver, Canada
maibin@bcit.ca
Abstract—In this paper, we compare the currently imple-
mented smart heating algorithms with our proposed method -
Weather Based Heating Algorithm. Our simulations are based on
the data from years 2015-2018, provided by Ecobee Inc., which
is one of the major manufacturers of smart thermostats. The
main problem that we focus on is the study of the overheated
homes. Our results showed that the problem of overheated
households during winter affects North America. The significant
impact is visible during sunny days. On the other hand, the
implementation of adaptive strategies, such as Weather Based
Heating algorithm, results in three times reduction of overheated
time and 20% reduction in energy costs.
Keywordsenergy, environment, home heating
I. INTRODUCTION
Over the last few years, we have seen an increased interest
in the Internet of Things solutions making their way into
residential homes. Nowadays, smart devices are inevitable in
our lives [1], [2]. Smart thermostats are one of them. In contrast
to regular thermostats, the smart ones allow to control the
climate at home remotely, show the energy consumption in
real-time, and can even adjust themselves based on ambient
conditions like humidity or temperature outside [3]–[5]. These
are the crucial features as residential heating uses more energy
than any other home expenditure including air conditioning,
humidification, and water heating [6]. It is essential to accom-
modate efficient techniques to increase the efficiency of HVAC
systems and decrease energy costs. Moreover, studies have
shown that more than half of the installed smart thermostats
are used in manual modes, due to the inefficiency of automatic
settings [7]–[9].
In this paper, we introduce a weather-based algorithm to
increase the efficiency of the heating using smart thermostats.
The standard algorithms work using very simple if else
statements. More advanced ones, use historical heating and
cooling run-times to control the HVAC system. Our proposed
algorithm uses an innovative approach that takes into an
account live weather forecasts to compute the impact on indoor
home temperature.
Several works have studied the problem for automatic
thermostat control. The majority of them were focusing on
the occupancy prediction. The authors of [10] introduced
occupancy prediction algorithms based on GPS-enabled smart-
phones. In [11], the thermostat control system is based on the
mobility prediction of users, using contextual information ob-
tained by mobile phones. Other papers [12]–[15] are using the
data analytics techniques to predict user behavior in terms of
home occupancy. Some authors are also focusing on proposing
energy scheduling methods, with both, the smart pricing tariffs
and the user comfort implication [16]–[19]. While occupancy
is an important factor to enable smart thermostat control, the
latest studies shown that people spend more than 75% of time
indoors [20], which makes it less significant for the efficient
home heating then the weather impact.
The remainder of the paper is organized as follows. In the
next Section, we introduce an optimization problem. Next, we
present the algorithms design. Finally, we provide simulation
setup and results discussion, followed by a conclusion.
II. OP TI MI ZATI ON P ROB LE M
A. Datasets
Datasets used in the simulations were provided by Ecobee
Inc., Canada. The company is the manufacturer of smart ther-
mostat devices. They established a research program named
Donate Your Data [21]. The data is shared from the home
devices in North America, from the years 2015-2018 and
consists of the following metadata fields: location; number of
occupants; heat/cool temperature settings; occupancy sched-
ules; indoor/outdoor temperature/humidity, HVAC runtimes.
Data from each thermostat is contained in an individual
.csv file, organized by a month and a year, using 5 minutes
intervals. The following symbols are used to describe it:
compHeat: heating runtime in seconds
dateTime: date and time that the reading was taken
schedule: three defaults states of the thermostat -
sleep/away/home - that are used to define the comfort
settings (preferred temperature)
Tctrl: current average temperature in the household
Tout: outdoor temperate
TstpHeat : indoor heat setpoint in the current schedule
t: time period
Moreover, we introduce the following variables:
heat: time to gain 1°C in particular household
lef t: time left before the change of the schedule
over: overheated time
B. Optimization problem
To introduce the optimization problem, let us as look at
the Figure 1. The data was obtained from the Ecobee device,
located in the Vancouver, Canada, during a sunny day. The
temperature setting for the day was 24°C and 21°C for the
night. At night, the temperature in the home was equal to
21°C. Around 1:00, the system turned on the heating to achieve
the set point for the day preference, which was set for 9:00.
The temperature in the house reached the desired value. At
11:35 the temperature was 0.5°C higher than preferred. Around
the 16:30 the indoor temperature reached the day high 26°C,
which was 2.5°C higher than the comfortable setting for this
household, due to the weather impact. The time that the
temperature was higher than the preference setting was an
example of overheated time - over.
Fig. 1: The example scenario
Thus, we want to optimize the following problem:
Objective
min
t
X
i=1
over(t), where over(t)Tctr l(t)TstpHeat(t)(1)
Subject to
Tctrl TstpHeat no longer than 2 hours (2)
over(t)/sleep away (3)
The formula (1) defines that we are minimizing the time
when the home is overheated (Tctrl(t)TstpHeat(t)), subject
to two constraints:
(2) - we cannot wait to heat up the household to reach the
comfortable temperature (TstpHeat ) for longer than 2
hours (system requirement)
(3) - the overheated time is not considered during sleep
or away schedules (after the analysis we can observe
that during these two stages homes are in 85% of
time not heating, just ”cooling down”)
III. ALGORITHMS
A. Standard Heating Algorithm
The logic in the Standard Heating (SH) algorithm is
straightforward - it turns the heating pump and the fan on when
Tctrl < TstpHeat . Please note that the measurement of data in
Ecobee Smart Thermostats is performed every 5 minutes; thus,
the compHeat time is set to 300 seconds.
B. History Based Heating Algorithm
The History Based Heating (HBH) algorithm calculates the
average time to heat up and cool down the house. It then
uses this information to determine the best way to use the
HVAC system to achieve the desired indoor temperature (see
the pseudo-code in Algorithm 1).
The procedure of home heating in case of Home sched-
ule is similar to the SH algorithm. Whenever the preferred
temperature is higher than the current one, the system turns
on the heating. The main difference is during Sleep and
Algorithm 1: History Based Heating Algorithm (HBH)
Data: Tctrl,TstpHeat ,schedule,lef t,heat
Result: compHeat
1Calculate the average heating time heat to gain 1°C;
2switch Schedule do
3case Home do
4while Tctrl < TstpHeat do
5Turn the fan and heating pump on;
6Set compHeat = 300s;
7break;
8end
9end
10 case Sleep/Away do
11 Read TstpHeat from the next Home schedule;
12 if TstpHeat Tctrl left heat then
13 Turn the fan and heating pump on;
14 Set compHeat = 300s;
15 break;
16 end
17 end
18 end
19 return compHeat;
Away schedules, where the algorithm calculates the average
household heating time to gain 1°C. It is then used to estimate
the correct time to start the heating before the next Home
schedule. It is called SmartRecovery - the algorithm prioritizes
the time when household members are awake, and start heating
earlier, so they can wake up in the comfortable temperature.
As we can observe in the lines 10-17 (case Sleep/Away), we
compare the difference between the two: the subtraction of
preferred temperature and the current one with the subtraction
of time remaining to the next Home schedule (left) and the
time needed to gain 1°C (heat). If the condition is fulfilled,
we then turn the heating on.
C. Weather Based Heating Algorithm
Our proposed solution, the Weather Based Heating (WBH)
algorithm, calculates the average time to heat up and cool down
the house in correlation to the weather based on the nearest
weather station (see Algorithm 2).
The idea behind the Away/Sleep schedules is the same as
for the HBH algorithm. The difference is during the Home
schedule. During this stage, we check if the current tempera-
ture is lower than preferred, but instead of turning the system
on, we run the predictHeating() method, shown in Algorithm 3.
First, we run a linear regression method on historical data to
calculate the average weatherImpact for every household. It
is a relation on how specific weather affects the temperature
inside, measured in °C per 5 minutes (e.g., 0.21°C per 5
minutes). It depends on multiple criteria: the current weather,
the temperature outside, the size of the household, the season
of the year and the time of the day. Next, we obtain the weather
forecast from DarkSky API. In the final step, we check the if-
else condition, using the average weather impact for the next
three hours (averageImpact), based on the obtained weather
forecast. If the condition from line 3 is met, we turn the fan
and heating pump on; otherwise, we set compHeat = 0;
Algorithm 2: Weather Based Heating Algorithm (WBH)
Data: Tctrl,TstpHeat ,schedule,lef t,heat,
weatherI mpact
Result: compHeat
1Calculate the average heating time heat to gain 1°C;
2switch Schedule do
3case Home do
4while Tctrl < TstpHeat do
5predictHeating() and set compH eat;
6break;
7end
8end
9case Sleep/Away do
10 Read TstpHeat from the next Home schedule;
11 if TstpHeat Tctrl left heat then
12 Turn the fan and heating pump on;
13 Set compHeat = 300s;
14 break;
15 end
16 end
17 end
18 return compHeat;
Algorithm 3: The predictHeating() method
Data: Tctrl,TstpHeat ,schedule,lef t,heat
Result: compHeat
1For every thermostat calculate weatherImpact;
2Using Dark Sky API check weather forecast;
3if TstpHeat Tctrl +averageImpact lef t heat
then
4Turn the fan and heating pump on;
5Set compHeat = 300s;
6break;
7end
8else
9return compHeat = 0;
10 end
IV. SIMULATION SETUP
Simulations were performed using data from three winters
(December-March), i.e., 2015-2018. We used data only from
Ecobee 3, Ecobee 3 Lite and Ecobee 4 devices, as they have
the same sensors to measure the temperature. The average
user preferences that we obtained from the data were as
follows: Tctrl = 20.6°C and TstpHeat = 21.7°C. The number
of households was equal 54000.
V. RESULTS
First, we want to present the computed values of
weatherI mpact, in Table I. As expected, the largest weather
impact on the household temperature was during sunny or
windy days. Sun and high temperature during the day caused
the increase of a maximum of 1.41°C per 5 minutes, whereas
the snowy weather during cold day impacted particular house-
hold with a maximum of -3.3°C per 5 minutes. Please note
that we calculated the impact during the moments where the
thermostats were in the Away schedule, which means that no
heating was involved. Moreover, we checked only the houses
without cooling systems. That allowed us to calculate the direct
weather impact on every single household.
TABLE I: The average ranges of weather impact and the
weather type categories
Category Impact Category Impact
Sunny (-25°C) -0.7°C:0.01°C Sunny (-25°C:-10°C) -0.3°C:0.31°C
Sunny (-10°C:0°C) 0.12°C:0.36°C Sunny (0°C:15°C) 0.28°C:0.43°C
Cloudy (-25°C) -1.25°C:-1.12°C Cloudy (-25°C:-10°C) -1.09°C:-0.81°C
Cloudy (-10°C:0°C) -1.01°C:-0.56°C Cloudy (0°C:15°C) -0.83°C:0.12°C
Snowy (-25°C) -3.24°C:-1.22°C Snowy (-25°C:-10°C) -2.3°C:-0.61°C
Snowy (-10°C:0°C) -0.88°C:-0.25°C Snowy (0°C:15°C) -0.55°C:-0.11°C
Windy (-25°C) -3.3°C:-2.1°C Windy (-25°C:-10°C) -2.71°C:-1.18°C
Windy (-10°C:0°C) -1.67°C:-0.6°C Windy (0°C:15°C) -0.81°C:0.32°C
Rainy (-25°C:-10°C) N/A Rainy (-10°C:0°C) -0.92°C:-0.02°C
Rainy (0°C:15°C) 0.20°C:0.73°C Rainy (15°C) 0.53°C:0.99°C
Another important observation was that during cloudy
days, the temperature did not increase much, even on hot days.
The direct sunlight, even with the lower outside temperature,
had more effect on the households than the weather during
the cloudy day with the higher temperature outside. Finally,
the snowy weather caused reduction of the indoor temperature,
even if the temperature outside was above 0°C.
Next, we present the maps related to the average temper-
ature outside, and the average overheated time of households,
during winters of 2015-2018, shown in Figure 2 and Figure 3,
respectively.
Fig. 2: The average temperature outside in North America in
winters of 2015/2016-2017/2018
Fig. 3: Overheated time for the households in North America
during winters of 2015/2016-2017/2018
There is a correlation between the average temperature
outside and the overheated time. In the regions with higher
temperature, even during the winter, the overheated time was
in a range of 20-30% of overall time. Moreover, we can notice
that houses in the most populated areas on the West and East
Coasts of North America were significantly overheated. The
gray area in the Northern regions indicates that we do not
have enough data.
Finally, we present the comparison of the algorithms that
were designed to reduce the overheated time, in Table II. We
compared three algorithms, presented in this paper, namely
Standard Heating (SH), History Based Heating (HBH) and
Weather Based Heating (WBH). The best algorithm was
TABLE II: Algorithms comparison for the overheated time.
Years SH HBH WBH
2015/2016 31.59% 20.83% 12.83%
2016/2017 33.22% 16.93% 12.86%
2017/2018 30.12% 14.02% 10.83%
WBH. It significantly reduced the overheated time of the
households to the range of 10-12% of the time, where the
worst, SH algorithm, resulted in around 30% overheated time.
The next observation is that History Based Heating algorithm
(HBH) improved in each subsequent winter, from 20.83% of
the overheated time to 14.02%. The ability to ”learn” about
the particular household bring the positive effect on the overall
performance in time.
We also analyzed the performance of the algorithms based
on the country, shown in Table III.
TABLE III: Algorithms comparison for the overheated time.
Country SH HBH WBH
Canada 29.36% 22.11% 12.44%
USA 23.31% 15.88% 12.94%
The trend was similar. The best algorithm was WBH, the
second best HBH, and the worst performance was shown
using SH algorithm. The interesting observation is that SH
and HBH were able to achieve better results in the USA than
in Canada. It is correlated to the results presented in Figure 3.
The overheated times of the houses in Montana, North Dakota
and Minnesota were much lower than in the other parts of the
United States, thus, it explains a slightly better performance
than in Canada.
The reduction of heating time in comparison to Standard
Heating algorithm is shown in Table IV. The usage of the
HBH and WBH algorithms allowed to save the time of the
heating in about 12-15%, and 19-21%, respectively. It means
that we also were able to save the costs of gas, electricity or
any energy sources.
TABLE IV: Heating time improvement
Year HBH WBH
2015/2016 12.71% 19.31%
2016/2017 14.58% 21.32%
2017/2018 13.71% 20.99%
VI. CONCLUSION
In this paper, we presented a study on the weather impact
on the households in North America. We compared three
algorithms, two used in current HVAC systems and one,
proposed in this paper. Results showed that the houses in North
America are overheated in around 20%-30% of the time. On
the other hand, the implementation of history based or weather-
related algorithms allows to increase the efficiency of home
heating, thus, reduce the costs of it.
ACKNOWLEDGMENTS
We gratefully acknowledge the support and contribution of
Ecobee Ltd. and Ecobee customers to this research.
REFERENCES
[1] H. Farhangi, “The path of the smart grid,” IEEE Power and Energy
Magazine, vol. 8, no. 1, pp. 18–28, 2010.
[2] G. Newe, “Delivering the internet of things,Network Security, vol.
2015, no. 3, pp. 18–20, 2015.
[3] C. Perera, C. H. Liu, S. Jayawardena, and M. Chen, “A Survey on
Internet of Things from Industrial Market Perspective,IEEE Access,
vol. 2, pp. 1660–1679, 2015.
[4] T. U. Daim and I. Iskin, “Smart thermostats: are we ready?” Interna-
tional Journal of Energy Sector Management, vol. 4, no. 2, pp. 146–151,
2010.
[5] J. Jacobson, “Info & Answers: Anatomy Lesson: Getting to Know Smart
Thermostats,” 2010.
[6] J. Torriti, “A review of time use models of residential electricity
demand,” pp. 265–272, 2014.
[7] M. Pritoni, A. K. Meier, C. Aragon, D. Perry, and T. Peffer, “Energy
efficiency and the misuse of programmable thermostats: The effective-
ness of crowdsourcing for understanding household behavior,Energy
Research and Social Science, vol. 8, pp. 190–197, 2015.
[8] T. Peffer, M. Pritoni, A. Meier, C. Aragon, and D. Perry, “How people
use thermostats in homes: A review,” pp. 2529–2541, 2011.
[9] J. Ansei, “Smart vs. Connected Thermostats: What’s the Difference?”
Air Conditioning Heating & Refrigeration News, vol. 256, no. 7, pp.
13–15, 2015.
[10] C. Koehler, B. Ziebart, J. Mankoff, and A. Dey, “TherML:
occupancy prediction for thermostat control,” Proceedings of
the 2013 . . . , pp. 103–112, 2013. [Online]. Available:
http://dl.acm.org/citation.cfm?id=2493441
[11] S. Lee, Y. Chon, Y. Kim, R. Ha, and H. Cha, “Occupancy prediction
algorithms for thermostat control systems using mobile devices,IEEE
Transactions on Smart Grid, vol. 4, no. 3, pp. 1332–1340, 2013.
[12] G. Hernandez, O. Arias, D. Buentello, and Y. Jin, “Smart Nest Ther-
mostat : A Smart Spy in Your Home,” Black Hat USA, pp. 1–8, 2014.
[13] J. Scott, A. Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, and
N. Villar, “PreHeat: Controlling Home Heating Using Occupancy Pre-
diction,” UbiComp ’11 Proceedings of the 13th international conference
on Ubiquitous computing, no. UbiComp, pp. 281–290, 2011.
[14] J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic,
E. Field, and K. Whitehouse, “The Smart Thermostat: Using Occupancy
Sensors to Save Energy in Homes,Proceedings of ACM SenSys,
vol. 55, pp. 211–224, 2010.
[15] Y. Liang, D. I. Levine, and Z. J. M. Shen, “Thermostats for the smart
grid: Models, benchmarks, and insights,” Energy Journal, vol. 33, no. 4,
pp. 61–95, 2012.
[16] D. T. Nguyen and L. B. Le, “Joint optimization of electric vehicle and
home energy scheduling considering user comfort preference,” IEEE
Transactions on Smart Grid, vol. 5, no. 1, pp. 188–199, 2014.
[17] H. T. Nguyen, D. Nguyen, and L. B. Le, “Home energy management
with generic thermal dynamics and user temperature preference,” in
2013 IEEE International Conference on Smart Grid Communications,
SmartGridComm 2013, 2013, pp. 552–557.
[18] K. F. Fong, V. I. Hanby, and T. T. Chow, “HVAC system optimization
for energy management by evolutionary programming,Energy and
Buildings, vol. 38, no. 3, pp. 220–231, 2006.
[19] S. Noh, J. Yun, and K. Kim, “An efficient building air conditioning
system control under real-time pricing,” APAP 2011 - Proceedings:
2011 International Conference on Advanced Power System Automation
and Protection, vol. 2, pp. 1283–1286, 2011.
[20] T. F. Sanquist, H. Orr, B. Shui, and A. C. Bittner, “Lifestyle factors
in U.S. residential electricity consumption,” Energy Policy, vol. 42, pp.
354–364, 2012.
[21] Ecobee Ltd., “Donate Your Data,” 2018. [Online]. Available:
https://www.ecobee.com/donateyourdata/
... One common area that utilizes IoT sensors is the HVAC system used in many modern buildings and homes. These systems are able to adjust the temperature to be the most comfortable and efficient for how many people are in a room using a technology called smart thermostats [2], [3]. ...
Conference Paper
Full-text available
Occupancy detection is crucial when trying to lower the emissions that a building produces. Some buildings are equipped with motion sensors or cameras to find how many occupants are in a room. However, this is not entirely accurate as people could be stationary in situations like sitting at a desk or watching television. Using environmental sensors, we can determine if a room is occupied even if the occupants are not moving. When occupants are inside a room, they give off extra CO2 or increase the room's temperature. We can find the small differences in the environmental values used to accurately predict a room's occupancy levels. We use relatively inexpensive IoT sensors that almost every building's HVAC system should have in the near future. We apply K-means clustering with success to predict occupancy levels. Our algorithms can be used in smart thermostats to automatically adjust the room's heat depending on how many occupants are in a room.
Article
Full-text available
These days, the majority of devices are connected to the Internet of Things. One of the areas that need further research is the smart thermostats’ operation. In this article, we perform an analysis of efficient household heating and air conditioning with smart thermostats. To the best of our knowledge, this is the first article that correlates real data from the smart thermostats and weather to predict heating, ventilation, and air conditioning (HVAC) operation. The main problem that we focus on is the study of overheated and overcooled homes. Our results show that adaptive strategies that learn the weather impact on the house enabled three times reduction of heating time and four times reduction of air conditioning, with the most significant improvement during sunny days.
Article
Full-text available
Abstract Programmable thermostats are generally sold as energy-saving devices controlling heating and cooling systems, but can lead to energy waste when not operated as designed by the manufacturers. We utilized Amazon Mechanical Turk, an online crowdsourcing service, to investigate thermostat settings and behavior in households. We posted a survey and paid respondents to upload pictures of their thermostats to verify self-reported data. About 40% of programmable thermostat owners did not use programming features and 33% had programming features overridden. Respondents demonstrated numerous misconceptions about how thermostats control home energy use. Moreover, we found that 57% of households were occupied nearly all the time, limiting the potential energy savings. The study revealed flaws in self-reported data, when collected solely from traditional surveys, which raises concerns about the validity of current thermostat-related research using such data. “Ground truth” temperature data could now be available in homes with Internet-connected thermostats. Online crowdsourcing platforms emerge as valuable tools for collecting information that would be difficult or expensive to obtain through other means. Advantages over traditional surveys include low-cost, rapid design–implementation–result cycle, access to diverse population, use of multimedia. Crowdsourcing is more effective than alternative online tools due to easier recruitment process and respondents’ reputation system.
Article
Full-text available
The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as radio frequency identifications, sensors, and actuators, as well as other instruments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen a plethora of IoT solutions making their way into the industry marketplace. Context-aware communications and computing have played a critical role throughout the last few years of ubiquitous computing and are expected to play a significant role in the IoT paradigm as well. In this paper, we examine a variety of popular and innovative IoT solutions in terms of context-aware technology perspectives. More importantly, we evaluate these IoT solutions using a framework that we built around well-known context-aware computing theories. This survey is intended to serve as a guideline and a conceptual framework for context-aware product development and research in the IoT paradigm. It also provides a systematic exploration of existing IoT products in the marketplace and highlights a number of potentially significant research directions and trends.
Conference Paper
Full-text available
Reducing the large energy consumption of temperature regulation systems is a challenge for researchers and practitioners alike. In this paper, we explore and compare two common types of solutions: A manual systems that encourages reduced energy use, and an intelligent automatic control system. We deployed an eco-feedback system with the ability to remotely control one's thermostat to ten participants for three months. Participants appreciated the ability to remotely control the thermostat, and controlled their heating system with 78.8% accuracy, a 6.3% improvement over not having this system. However, despite having feedback and remote control, they still wasted a lot of energy heating when away from home for the day. Using data from our deployment, we developed TherML, an occupancy prediction algorithm that uses GPS data from a user's smartphone to automatically control the indoor temperature of a home with 92.1% accuracy. We compare TherML to other state-of-the-art techniques, and show that the higher accuracy of our approach optimizes both energy usage and user comfort. We end with recommendations for a mixed initiative system that leverages aspects of both the manual and automated approaches that can better match heating control to users' routines and preferences.
Article
The Internet of Things-objects and appliances with embedded sensors and chips capable of communicating online-will result in 50 billion devices being connected to the Internet by 2020, according to Gartner.1 From fridges and bathroom scales, to fitness bands and home thermostats, the number of 'things' connected to the Internet is really taking off and it's a very exciting time for everyone. However, for many enterprises and consumers, the excitement of this new realm of connectivity is clouding the fact that, with more devices connected to the network, there comes a new array of security implications.
Article
Residential electricity demand in most European countries accounts for a major proportion of overall electricity consumption. The timing of residential electricity demand has significant impacts on carbon emissions and system costs. This paper reviews the data and methods used in time use studies in the context of residential electricity demand modelling. It highlights key issues which are likely to become more topical for research on the timing of electricity demand following the roll-out of smart metres.
Conference Paper
In this paper, we develop a novel home energy management solution that aims to minimize the electricity cost and guarantee user comfort in terms of preferred home temperature. Specifically, we consider a typical household system with a Heat Ventilation and Air Conditioning (HVAC) system and various types of loads. We formulate the home energy scheduling problem considering generic thermal dynamics represented by a look-up table, thermal comfort constraints, and specific characteristics of different electric loads. The assumed generic thermal dynamics overcome limitations of other approximate equation-based thermal dynamics typically employed in the literature. However, the empirical thermal dynamics makes the energy scheduling problem a complicated non-linear optimization problem, which is difficult to tackle. Therefore, we develop a decomposed solution approach where the scheduling of HVAC system and other loads are optimized in two different steps. We show that the HVAC scheduling problem is a dynamic programming problem and develop an algorithm to find its optimal solution considering the user comfort constraint Given the optimal HVAC scheduling solution, the scheduling problem for remaining loads is transformed into a mixed integer program whose solution can be found by using an available optimization solver. We then present numerical results to demonstrate the effectiveness and correctness of our proposed solution and its relative performance compared with the conventional design.
Article
In this paper, we investigate the joint optimization of electric vehicle (EV) and home energy scheduling. Our objective is to minimize the total electricity cost while considering user comfort preference. We take both household occupancy and EV travel patterns into account. The novel contributions of this paper lie in the exploitation of EVs as dynamic storage facility as well as detailed modeling of user comfort preference, thermal dynamics, EV travel, and customer occupancy patterns in a concrete optimization framework. Extensive numerical results are presented to illustrate the efficacy of the proposed design. Specifically, we show that the proposed design can achieve significant saving in electricity cost, allow more flexibility in setting the tradeoff between cost and user comfort, and enable to reduce energy demand during peak hours. We also demonstrate the benefits of applying the proposed framework to a residential community compared to optimization of individual household separately.
Article
Several techniques have been proposed for the automatic control of just-in-time heating and cooling systems in indoor spaces that accommodate both the occupants' comfort and energy savings. Current techniques, however, do not provide an adequate solution for efficient thermostat control, because they require costly infrastructures to detect occupancy or because they inaccurately predict the occupancy due to irregular patterns. In this paper, we propose an automatic thermostat control system based on the mobility prediction of users, using contextual information obtained by mobile phones. We also present an arrival time prediction scheme that combines both historical pattern and route classification. The experimental results indicate that the proposed system can successfully predict at least 70% of the transit cases within 10 minutes' error and can decrease energy consumption by 26%.
Article
We model two existing thermostats and one novel thermostat to see how well they operate under dynamic pricing. The existing thermostats include a traditional thermostat with set temperature goals and a rigid thermostat that minimizes cost while always keeping temperature within a rigid predetermined range. We contrast both with a novel optimizing thermostat that finds the optimal tradeoff between comfort and cost. We compare the thermostats’ performance both theoretically and via numerical simulations. The simulations show that, under plausible assumptions, the optimizing thermostat’s advantage is economically large. Importantly, the electricity demand of the rigid thermostat (but not the optimizing thermostat) ceases to respond to electricity prices on precisely the days when the electricity grid tends to be near capacity. These are the times when demand response is the most socially valuable to avoid massive price spikes. The social benefits of the optimizing thermostat may provide incentives for utilities and regulators to encourage its adoption.