Deep Learning Innovations for Energy Efficiency: Advances in Non-Intrusive Load Monitoring and EV Charging Optimization for a Sustainable Grid
It addresses energy efficiency and CO2 reduction for residential and transport sectors, but appears incremental as it applies existing deep learning methods to specific energy problems.
This dissertation tackles the problem of accelerating the energy transition by developing novel Deep Learning techniques for Non-Intrusive Load Monitoring to reduce residential energy consumption and for EV charging optimization using Deep Reinforcement Learning to decarbonize road transport, aiming to create tools that address limitations in these domains.
The global energy landscape is undergoing a profound transformation, often referred to as the energy transition, driven by the urgent need to mitigate climate change, reduce greenhouse gas emissions, and ensure sustainable energy supplies. However, the undoubted complexity of new investments in renewables, as well as the phase out of high CO2-emission energy sources, hampers the pace of the energy transition and raises doubts as to whether new renewable energy sources are capable of solely meeting the climate target goals. This highlights the need to investigate alternative pathways to accelerate the energy transition, by identifying human activity domains with higher/excessive energy demands. Two notable examples where there is room for improvement, in the sense of reducing energy consumption and consequently CO2 emissions, are residential energy consumption and road transport. This dissertation investigates the development of novel Deep Learning techniques to create tools which solve limitations in these two key energy domains. Reduction of residential energy consumption can be achieved by empowering end-users with the user of Non-Intrusive Load Monitoring, whereas optimization of EV charging with Deep Reinforcement Learning can tackle road transport decarbonization.