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 minimise 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 communual 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 0.95 • C and 1.37 • C for 2 and 4 hours predictions, respectively; in contrast to the corresponding 2.09 • C and 3.11 • C errors of the existing (historical-average based) thermal model.

Advanced Economic Control of Electricity-Based Space Heating Systems in Domestic Coalitions with Shared Intermittent Energy Resources

“…On the other hand, adaptive gray-box approaches rely on simplified physical equations and, as such, do not suffer from the above drawbacks [Li and Wen 2014]. Such approaches have already shown their potential for thermal modeling in highly dynamic settings (e.g., [Reece et al 2014;Alam et al 2014]). Moreover, AdaHeat demonstrates the value of such an approach within the context of a DHAS.…”

“…This becomes even more apparent in domestic settings where the occupant's activity can drastically alter the thermal dynamics (e.g., opening a window, cooking, or operating an auxiliary fan heater). That said, adaptive thermal modeling has been shown to be resilient and effective in such highly dynamic settings and several adaptive thermal modeling approaches have been proposed over time (e.g., [Ruano et al 2006;Reece et al 2014;Alam et al 2014]). These can be classified into either black-box or gray-box approaches [Prívara et al 2013].…”