Inferring Smart Schedules for Dumb Thermostats

Abstract

Heating, ventilation, and air conditioning (HVAC) accounts for over 50% of a typical home’s energy usage. A thermostat generally controls HVAC usage in a home to ensure user comfort. In this article, we focus on making existing “dumb” programmable thermostats smart by applying energy analytics on smart meter data to infer home occupancy patterns and compute an optimized thermostat schedule. Utilities with smart meter deployments are capable of immediately applying our approach, called iProgram, to homes across their customer base. iProgram addresses new challenges in inferring home occupancy from smart meter data where (i) training data is not available and (ii) the thermostat schedule may be misaligned with occupancy, frequently resulting in high power usage during unoccupied periods. iProgram translates occupancy patterns inferred from opaque smart meter data into a custom schedule for existing types of programmable thermostats, e.g., 1-day, 7-day, and so on. We implement iProgram as a web service and show that it reduces the mismatch time between the occupancy pattern and the thermostat schedule by a median value of 44.28min (out of 100 homes) when compared to a default 8am-6pm weekday schedule, with a median deviation of 30.76min off the optimal schedule. Further, iProgram yields a daily energy savings of 0.42kWh on average across the 100 homes. Moreover, the schedules generated from iProgram converge to optimal schedules within a couple of weeks for most homes. We also show that homeowners having multiple HVAC zones can utilize iProgram and potentially increase unconditioned times of less occupied parts of their homes by 70%. Utilities may use iProgram to recommend thermostat schedules to customers and provide them estimates of potential energy savings in their energy bills.

Going smart, staying confused: Perceptions and use of smart thermostats in British homes

“…An initial schedule which matches the residents' occupancy patterns could go some way to avoid the inefficient heating behaviour observed in this study and in e.g. [50], [51]. However, the aforementioned possibility of frequent schedule variation is important, not only due to the increase in interaction, but also to the increase in self-reported usability of the heating controls, which means that this variation could continue long-term.…”

“…Reinforcing the potential of occupancy patterns in predicting demand, [50] find that algorithms for schedule-based occupancy prediction can achieve forecasting accuracies of over 80%, and [39] highlight the importance of using real-world occupancy data to model building energy demand. In addition to potentially improving demand forecasting, accurate heating schedules based on occupancy patterns have also been linked to energy-saving potential across several studies: in the US, [51] estimate that applying accurate heating schedules could save between 1% and 5% in HVAC-related electricity consumption; [6] find that automatic occupancy sensing in smart thermostats could generate average HVAC energy savings of 28%; and [52] further highlight the importance of using accurate occupancy patterns to optimize energy use.…”

Using personal environmental comfort systems to mitigate the impact of occupancy prediction errors on HVAC performance

“…The occupancy-based control allows buildings to operate outside of comfort regimes when unoccupied, thus reducing energy usage [17]. Henceforth, several other studies also explored the use of occupancy information to optimize the HVAC energy operations [2,18,17,19,20,21,22,23,24,25]. However, centralized HVAC controllers divide a building into thermal zones comprising of private and shared spaces.…”

iSchedule

“…Thus, by deriving repeating occupancy-based schedules, we enable facility managers to retain some control over HVAC usage. Residential versus Commercial: In residential settings, eorts such as smart thermostat [17], iProgram [12], as well as products such as Nest, Ecobee, and Lyric, have been used to improve HVAC energy-eciency. Such smart thermostats, as well as all "dumb" programmable thermostats, use schedule-based HVAC control, where occupancy information (from onboard sensors, phone GPS, or even electricity meters [12]) is analyzed to automatically learn a custom schedule.…”

“…In recent years, there has been signicant work in the research community and industry to optimize HVAC energy usage in buildings. For example, smart thermostats [12,17] use sensors to track occupancy patterns within a home, and then analyze these patterns to automatically learn and program a thermostat schedule. Many smart thermostats are now commercially-available for residential use, including the Honeywell Lyric [18], Nest [21], and Ecobee [8].…”