Abstract
We have developed a spatiotemporal heating control algorithm for use in homes. This system utilises a combination of relatively low-tech hardware interfaced with electric heating systems and a smartphone interface to this hardware, and a central server that progressively learns users' room-specific presence profiles and thermal preferences. This paper describes the associated spatiotemporal heating control algorithm, its evaluation utilising the dynamic building performance simulation software EnergyPlus, and a longitudinal deployment of the algorithm controlling a quasi-autonomous spatiotemporal home heating system in three domestic homes. In this we focus on the prediction of occupants' presence and preferred set-point temperature as well as on the calculation of optimum start time and the utilisation of user-scheduled absences; this for two comfort strategies: to maximise comfort and to minimise discomfort. The former aims to deliver conditions equating to a ‘neutral’ thermal sensation, whereas the latter targets a ‘slightly cool’ sensation with corresponding heating energy savings. Simulation results confirmed that the algorithm functions as intended and that it is capable of reducing energy demand by a factor of seven compared with EnergyStar recommended settings for programmable thermostats. Field study results align with these findings and highlight the possibility to reduce energy under the minimise discomfort strategy without compromising on occupants' thermal comfort.
Highlights
This research is motivated by the IPCC's recommendation to achieve a 40–70% reduction in anthropogenic greenhouse gas emissions by 2050 and to fully decarbonise anthropogenic activities by 2100, to maintain global warming below 2 °C over the course of the 21st century [24]
We conclude that the algorithm succeeds in adapting itself to users' thermal preferences and accommodates diversity in these preferences between housing and heating system configurations; with constraints on heating system capacity leading to offsets in median indoor temperature that are typically observed in poorly insulated housing
An accurate assessment would require measurements of energy use, both pre- and post-intervention. The aim of this exercise was to evaluate the fitness for purpose and real life performance of a quasi-autonomous spatiotemporal home heating control algorithm
Summary
This research is motivated by the IPCC's recommendation to achieve a 40–70% reduction in anthropogenic greenhouse gas emissions by 2050 and to fully decarbonise anthropogenic activities by 2100, to maintain global warming below 2 °C over the course of the 21st century [24]. Subsequent work highlighted that a probabilistic presence schedule derived from GPS data outperformed user-reported presence schedules and driving home duration alone [17], indicating that an automated system could deliver better results for limiting heater switch-on time than a human-programmed thermostat None of these studies applied these schedules to a simulated or situated heating system, not reflecting the complexities of managing a thermal environment to match users' expectations; nor did they adapt set-points according to users' preferences or exercise spatial discrimination in their control. From this review of the key advances in advanced home heating control systems, we conclude that significant effort has been invested in strategies to predict occupancy, using a variety of data sources, to best match pre-heating and heating output with presence Those studies that have incorporated real-life deployment have treated the thermal comfort feedback loop as closed, so that preferred heating set-point was not included as a control variable; and few of these have addressed the domestic setting. We refer the interested reader to Kruusimägi (2017) for a more detailed review of advances in home heating control systems and of joint-cognitive systems approaches [12] to include human subjects in their design and subsequent deployment, with the aim of maximising the dual objectives of acceptance and performance gains
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