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Applying extended Kalman filters to adaptive thermal modelling in homes

Applying extended Kalman filters to adaptive thermal modelling in homes
Applying extended Kalman filters to adaptive thermal modelling in homes

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 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.

extended Kalman filter, Heating systems, thermal modelling, underfloor heating
1751-2549
48-65
Alam, Muddasser
f2a6ba6a-7dee-4083-afa8-47910bfc1b9b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c
Scott, James
38da9dc5-ce87-4dc9-96c9-14af6592c2fb
Ali, Kamran
480d8696-6882-488d-ba38-c0d2dc5b69e6
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d
Alam, Muddasser
f2a6ba6a-7dee-4083-afa8-47910bfc1b9b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c
Scott, James
38da9dc5-ce87-4dc9-96c9-14af6592c2fb
Ali, Kamran
480d8696-6882-488d-ba38-c0d2dc5b69e6
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d

Alam, Muddasser, Rogers, Alex, Scott, James, Ali, Kamran and Auffenberg, Frederik (2018) Applying extended Kalman filters to adaptive thermal modelling in homes Advances in Building Energy Research, 12, (1), pp. 48-65. (doi:10.1080/17512549.2017.1325398).

Record type: Review

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 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.

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More information

Accepted/In Press date: 12 March 2017
e-pub ahead of print date: 16 May 2017
Published date: 2 January 2018
Keywords: extended Kalman filter, Heating systems, thermal modelling, underfloor heating

Identifiers

Local EPrints ID: 417416
URI: https://eprints.soton.ac.uk/id/eprint/417416
ISSN: 1751-2549
PURE UUID: 1fac0fb4-9abc-4f2c-a7b2-728976e0e6d8

Catalogue record

Date deposited: 31 Jan 2018 17:30
Last modified: 31 Jan 2018 17:30

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Contributors

Author: Muddasser Alam
Author: Alex Rogers
Author: James Scott
Author: Kamran Ali
Author: Frederik Auffenberg

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