Applying extended Kalman filters to adaptive thermal modelling in homes

ABSTRACT

Space-heating accounts for more than 40% of residential energy consumption in some countries (e.g. the UK and the US) and thus is a key area to address for energy efficiency improvement. To do so, intelligent domestic heating systems (IDHS) equipped with sensors and technologies that minimize user-input, have been proposed for optimal heating control in homes. However, a key challenge for IDHS is to obtain sufficient knowledge of the thermal dynamics of the home to build a thermal model that can reliably predict the spatial and temporal effects of its actions (e.g. turning the heating on or off or use of multiple heaters). This challenge of learning a thermal model has been studied extensively for decades in large purpose-built buildings (such as offices, educational, commercial or communal residential buildings) where machine learning is used to infer suitable thermal models. However, we believe that the technological gap between homes and buildings is fast vanishing with the advent of home automation and cloud computing, and the techniques and lessons learned in purpose-built buildings are increasingly applicable to homes too; with necessary modifications to tackle the challenges unique to homes (e.g. impact of household activities, diverse heating systems, more lenient occupancy schedule). Following this philosophy, we present a methodical study where stochastic grey-box modelling is used to develop thermal models and an extended Kalman filter (EKF) is used for parameter estimation. To demonstrate the applicability in homes, we present the case-study of a room in a family house equipped with underfloor heating and custom-built .NET Gadgeteer hardware. We built grey-box models and use the EKF to infer the thermal model of the room. In doing so, we use our in-house collected data to show that, in this instance, our thermal model predicts the indoor air temperature where the 95th percentile of the absolute prediction error is and for 2 and 4 hours predictions, respectively; in contrast to the corresponding and errors of the existing (historical-average based) thermal model.

KEYWORDS:

• Heating systems
• thermal modelling
• extended Kalman filter
• underfloor heating

Acknowledgments

The authors are thankful to Steven Reece, University of Oxford, for discussions on the use of the EKF.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Available at http://sel.me.wisc.edu/trnsys/ and http://apps1.eere.energy.gov/buildings/energyplus

2 We use linear regression to estimate where needed.

3 https://www.cl.cam.ac.uk/research/dtg/weather/

4 http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php

5 see http://www.telegraph.co.uk/property/3345024/Will-you-really-feel-the-heat-under-your-feet.html

6 An example is our previously developed Smart Home Framework that enables rapid modelling of smart homes and communities (Alam, Alan, Rogers, & Ramchurn, 2013). Available online at http://www.smarthomeframework.ecs.soton.ac.uk/

Additional information

Funding

University of Southampton was supported by the EPSRC grant EP/K503770/1 under the Impact Acceleration Account.