Demand Side Management in Homes with the Human Factor in the Loop

 

picture-35-1351286030 Long Tran-Thanh
Lecturer (Assistant Professor)
University of Southampton

February 10, 2017
3:30–4:30pm
Science Hall 124
Host: Son Tran and William Yeoh

Abstract

In this talk I will describe a number of demand side management projects we have developed in Southampton, and our latest work on how we deal with the related challenges of considering homeowners’ behaviourial models, such as bother cost/user annoyance. In particular, we address the problem of recommending an appliance usage schedule to the homeowner which balances between maximising total savings and maintaining sufficient user convenience. An important challenge within this problem is how to elicit the user preferences with low intrusiveness, in order to identify new schedules with high cost savings, that still lies within the user’s comfort zone. To tackle this problem we propose iDR, an interactive system for generating personalised appliance usage scheduling recommendations that maximise savings and convenience with minimal intrusiveness. In particular, our system learns when to stop interacting with the user during the preference elicitation process, in order to keep the bother cost (e.g., the amount of time the user spends, or the cognitive cost of interacting) minimal.

Biography

Long is a Hungarian-Vietnamese computer scientist at the University of Southampton, UK, where he is a Lecturer (Assist. Prof.) in Computer Science. Long did his university studies in Budapest, Hungary (BME-VIK) and obtained his PhD from Southampton in 2012, under the supervision of Nick Jennings and Alex Rogers. He has been doing active research in a number of key areas of AI, mainly focusing on online machine learning, game theory, and incentive engineering. For his work, he has received a number of prestigious awards, such as:

  • the CPHC/BCS PhD Dissertation Award (for the best Computer Science PhD thesis in the UK in 2012/2013) – Honourable Mention;
  • the ECCAI Artificial Intelligence Dissertation Award (for the best European PhD thesis in AI in 2012) – Honourable Mention;
  • the Association for the Advancement of Artificial Intelligence (AAAI) Outstanding Paper 2012 Award – Honourable Mention; and
  • the European Conference on Artificial Intelligence (ECAI) Best Student Paper 2012 Award – Runner-Up.