Monday, 23 September 2013

BuildSys 2013 Interesting Papers

The 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys) is coming up in November and I wanted to share a few papers which have recently been accepted there. Although the camera ready submissions weren't due at the time of writing, I've managed to get hold of a pre-print of a few interesting papers, which the authors are happy for me to share.

  • Towards a Smart Home Framework, Muddasser Alam, Alper T. Alan, Alex Rogers, and Sarvapali D. Ramchurn. Agents, Interaction and Complexity Research Group, University of Southampton, UK.
    • This paper presents the Smart Home Framework simulation platform for modelling smart homes. The platform provides extendable building blocks for smart households, such as micro-generation, energy storage, in addition to the components of more traditional homes, such as household electronics and heating. The framework has been designed to easily enable the simulation of different household environments in order to test the potential for different smart technologies.
  • It’s Different: Insights into home energy consumption in India, Nipun Batra, Manoj Gulati, Amarjeet Singh, Mani B. Srivastava. Indraprastha Institute of Information Technology, Delhi, India & University of California Los Angeles, United States.
    • This paper presents a new data set called Home Deployment, collected from a single household in Delhi. The authors describe many factors which distinguish the data set from other data sets collected from developed countries, such as the unreliability of the electrical grid and Internet connectivity. The data spans 73 days, collected from household-level, circuit-level, and appliance-level meters.
  • A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households, Alex Rogers, Siddhartha Ghosh, Reuben Wilcock and Nicholas R. Jennings. University of Southampton, UK.
    • This paper presents MyJoulo, a low-cost hardware solution which provides personalised home heating advice to households. The system consists of a single temperature logger which is placed on top of a household's thermostat, which is able to learn the thermal properties of a household. The thermal model is then used to provide feedback to the household occupants by comparing your learned thermostat set point to the national average, in addition to estimating the the potential savings should the set point be reduced or the timer settings changed.

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