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My PhD project focuses on applying reinforcement learning (RL) to building energy optimisation. The make-up of electrical grids is changing: there is an increasing number of energy systems that involve renewable energy generation, energy storage, smart controllable devices, electric vehicle charging and other recently improved technologies. As an example of the rapid rate of change, the global capacity of solar photovoltaic installations has been estimated to have increased by over 700 percent between 2011 and 2019. The energy systems introduced by these changes raise complex control problems. If controlled well, the systems may be able to effectively replace emission-intensive grid energy with local renewable energy and prevent demand peaks that would need to be covered by fossil-fuel generators. Thereby, controllers that are well adapted for these systems have the potential to help mitigate climate change. Existing control methods, such as model predictive control, often lack the flexibility to fully capture the potential cost and emission savings enabled by these systems. In this PhD project I aim to investigate the use of reinforcement learning (RL) in place of such conventional energy system controllers. RL is a general machine learning-based control method that may provide more flexibility than other existing methods. Within the last ten years, the integration deep neural networks in RL methods has allowed for RL to be used to outperform human level performance for the first time for several tasks, including at Atari games and the board game of Go. Building on this work, and other work applying RL to energy systems, this project aims to investigate how RL can be best used to improve energy efficiency in buildings. The initial focus of the project is on a specific kind of residential energy system that combines solar photovoltaic panels with a home battery. Based on the findings from this specific case, more general solutions in the space will be investigated.

Gambling is a widespread behaviour that around 70% of the British population engage in at least occasionally. In some individuals, gambling spirals out of control and takes on the features of an addiction. Research into the causes of drug addiction in humans is hampered by the fact that the drugs themselves often have a damaging effect on the brain, so that it is very difficult to separate the causes from the consequences of the addiction. As a ?behavioural addiction? where no drug is consumed, problem gambling may offer some important insights into the mechanisms by which all addictions develop. The current proposal is concerned with ?pathological gambling?, the most severe form of the disorder, and we will recruit a large group of these subjects as they begin a treatment program at a specialist NHS clinic in London. Little is known about the psychological make-up of pathological gamblers in the UK, because few treatment facilities were available until recently. We will compare pathological gamblers (n=100) against healthy non-gamblers (n=50) on questionnaire measures and a computerised assessment of impulsivity (the tendency to make quick or unplanned actions) and compulsivity (the tendency to repeat an action when it is no longer productive). These psychological constructs are central to recent theories of addiction. We expect that around half our gamblers will also be addicted to either alcohol, nicotine or illicit drugs, and we will compare gamblers with and without substance addictions on the psychological assessment. We will monitor how the gamblers? symptoms improve with treatment, at a 12- and 24- week follow-up, in order to test whether psychological function at the beginning of treatment can be used to predict which gamblers will respond best. Finally, we will recruit 6 gamblers and 6 non-gamblers for a brain scan using a radioactivity-labelled dopamine drug, in order to measure levels of dopamine function in the gamblers. Changes in dopamine function are a robust finding in the substance addictions, but have not been examined in the behavioural addictions. The brain imaging data will constitute a feasibility pilot study for a larger funding bid to look at brain chemistry in problem gambling.

MRes-The programme is designed to develop the following broad themes: Component (compressor, combustor, turbine) aerodynamics System-level design and component integration Methods (experimental and computational) for aerodynamic research and design Researcher skills Experience of university research groups and industry facilities