The University of Southampton
University of Southampton Institutional Repository

Adaptive Home Heating Control Through Gaussian Process Prediction and Mathematical Programming

Adaptive Home Heating Control Through Gaussian Process Prediction and Mathematical Programming
Adaptive Home Heating Control Through Gaussian Process Prediction and Mathematical Programming
In this paper, we address the challenge of adaptively controlling a home heating system in order to minimise cost and carbon emissions within a smart grid. Our home energy management agent learns the thermal properties of the home, and uses Gaussian processes to predict the environmental parameters over the next 24 hours, allowing it to provide real time feedback to householders concerning the cost and carbon emissions of their heating preferences. Furthermore, we show how it can then use a mixed-integer quadratic program, or a computationally efficient greedy heuristic, to adapt to real-time cost and carbon intensity signals, adjusting the timing of heater use in order to satisfy preferences for comfort whilst minimising cost and carbon emissions. We evaluate our approach using weather and electricity grid data from January 2010 for the UK, and show our approach can predict the total cost and carbon emissions over a day to within 9%, and show that over the month it reduces cost and carbon emissions by 15%, and 9%, respectively, compared to using a conventional thermostat.
71-78
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Maleki, Sasan
85222410-87d4-44eb-8721-fae2612b7721
Ghosh, Siddhartha
abaf1e1d-3b5f-4a61-913e-e61273ed3790
Jennings, Nicholas R,
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Maleki, Sasan
85222410-87d4-44eb-8721-fae2612b7721
Ghosh, Siddhartha
abaf1e1d-3b5f-4a61-913e-e61273ed3790
Jennings, Nicholas R,
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Rogers, Alex, Maleki, Sasan, Ghosh, Siddhartha and Jennings, Nicholas R, (2011) Adaptive Home Heating Control Through Gaussian Process Prediction and Mathematical Programming. Second International Workshop on Agent Technology for Energy Systems (ATES 2011), Taipei, Taiwan. pp. 71-78 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we address the challenge of adaptively controlling a home heating system in order to minimise cost and carbon emissions within a smart grid. Our home energy management agent learns the thermal properties of the home, and uses Gaussian processes to predict the environmental parameters over the next 24 hours, allowing it to provide real time feedback to householders concerning the cost and carbon emissions of their heating preferences. Furthermore, we show how it can then use a mixed-integer quadratic program, or a computationally efficient greedy heuristic, to adapt to real-time cost and carbon intensity signals, adjusting the timing of heater use in order to satisfy preferences for comfort whilst minimising cost and carbon emissions. We evaluate our approach using weather and electricity grid data from January 2010 for the UK, and show our approach can predict the total cost and carbon emissions over a day to within 9%, and show that over the month it reduces cost and carbon emissions by 15%, and 9%, respectively, compared to using a conventional thermostat.

Text
paper.pdf - Author's Original
Download (692kB)

More information

Published date: 2 May 2011
Additional Information: Event Dates: May 2011
Venue - Dates: Second International Workshop on Agent Technology for Energy Systems (ATES 2011), Taipei, Taiwan, 2011-05-02
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272235
URI: http://eprints.soton.ac.uk/id/eprint/272235
PURE UUID: 7abcc907-2e4f-41a4-8586-c9a821982bda

Catalogue record

Date deposited: 28 Apr 2011 22:34
Last modified: 14 Mar 2024 09:50

Export record

Contributors

Author: Alex Rogers
Author: Sasan Maleki
Author: Siddhartha Ghosh
Author: Nicholas R, Jennings

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×