Supervisory Team: Enrico Gerding, Sebastian Stein, Jameson Brouwer
The smart grid enables flexible demand and controlling devices based on fluctuating prices, resulting in savings for individuals and reduced carbon emissions, especially when combined with energy storage devices (such as batteries and electric vehicles), and heating and cooling, where there is such flexibility. A challenge is optimising the control of devices, requiring predicting future prices and demand, and optimising decisions over a longer time horizon.
This project will build on existing work and focus on the engagement with the user in smart grid autonomous systems. The challenges here are three-fold. First of all, for the user to trust the decisions that are made by the autonomous system and engage with the system in a constructive way, these decisions need to be presented in an intelligible and easy to understand manner. Techniques from explainable artificial intelligence will be used for this purpose.
Second, to produce optimal decisions on behalf of the user, it needs to understand the user’s preferences and constraints, e.g. when they are at home, what their comfort level is, and trade offs between costs and convenience. The system can infer these preferences indirectly by observing the user, or it can ask explicitly, but this causes additional burden to the user. This part of the project will explore preference elicitation techniques combined with human-in-the-loop reinforcement learning to infer the user model with minimal input from the user.
Finally, the project will consider the user incentives to reduce carbon emissions. The project will use techniques from game theory to ensure that the user has no incentive to manipulate the system (e.g. by misrepresenting their flexibility for demand or personal comfort preferences).
In summary, this exciting PhD project will combine optimisation, machine learning, explainable AI, human-computer interaction, as well as techniques from behavioural economics and game theory. There is considerable flexibility in terms of the scope and focus of the project. However, one of the outcomes involves building a proof of concept through a mobile application and test it with real users. Therefore, good programming skills are essential.
Your primary supervisors will be Dr Enrico Gerding and Dr Sebastian Stein, who are leaders in the field of intelligent agents and their application to energy management. In addition, the PhD project is co-funded and co-supervised by EKM Metering, a smart metering company based in California, U.S. During your PhD, you will be based in the School of Electronics and Computer Science (ECS) at the University of Southampton, and meetings with EKM Metering will be done largely via conference calls.
Equality, diversity and Inclusion is central to the ethos in ECS. We particularly encourage women, Black, Asian and minority ethnic (BAME), LGBT and disabled applicants to apply for this position.
Undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: 31 March 2021.
Funding: UK students, Tuition Fees and a stipend of £15,609 per annum for up to 3.5 years.
How To Apply
Apply online here, programme type (Research), Faculty of Physical Sciences and Engineering, “PhD Computer Science (Full time)”. In Section 2 you should insert the name of the supervisor Enrico Gerding
Applications should include:
Two reference letters
Degree Transcripts to date