Machine learning and multimedia content generation for energy demand reduction

N.H. Goddard, J.D. Moore, C.A. Sutton, H. Lovell, J. Webb

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Domestic energy demand accounts for about 30% of overall energy use. The IDEAL project uses a variety of IT methods to investigate whether, and in which social groups, feedback of personalised, household-specific and behaviour-specific information results in greater reduction in energy use than overall consumption information reported by Smart Meters. It is a sociotechnical study, concentrated on existing housing, with a strong social science component and an experimental design that looks at income levels and household composition as primary factors. Temperature and humidity data related to behaviour is gathered using a small number of wireless sensors in the home, together with data on weather, building factors and household composition. This data is streamed over the internet to servers where it is analysed using Bayesian machine-learning methods to extract household-specific behaviours in near-realtime. Information on the cost, carbon content and amount of energy used for specific behaviours is reported back to the householders via a dedicated wireless tablet. This interactive content is automatically generated using multimedia methods based on natural language generation techniques. The project is in its design phase, with the main project planned (and funded) to run 2013-2016. It is anticipated to demonstrate whether such low-cost sensing, analysis and feedback is significantly more effective than standard Smart Meters in reducing demand, and a business opportunity for green service organisations.
Original languageEnglish
Title of host publicationSustainable Internet and ICT for Sustainability (SustainIT), 2012
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)978-1-4673-2031-3
Publication statusPublished - 2012


  • learning (artificial intelligence)
  • media streaming
  • carbon content
  • demand side management
  • green service organisations
  • belief networks
  • domestic energy demand
  • personalised information
  • energy demand reduction
  • IT methods
  • wireless sensor networks
  • Wireless sensor networks
  • meteorology
  • building energy efficiency
  • low-cost sensing
  • building management systems
  • Humidity
  • demand reduction
  • multimedia content generation
  • green computing
  • household-specific behaviours
  • household-specific information
  • Bayesian machine learning method
  • notebook computers
  • low-cost analysis
  • humidity sensors
  • weather data
  • natural language generation
  • business opportunity
  • social groups
  • human-computer interaction
  • Internet
  • wireless sensors
  • Temperature sensors
  • interactive systems
  • Electricity
  • socio-economic effects
  • data streaming
  • energy use reduction
  • cost information
  • sociotechnical study
  • Gas detectors
  • wireless tablet
  • natural language generation techniques
  • temperature data
  • household composition
  • natural language processing
  • humidity data
  • IDEAL project
  • income levels
  • behaviour-specific information
  • Heating
  • automatic interactive content generation
  • temperature sensors
  • machine learning
  • building factors


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