Part II/ACS projects (2022)
- Reinforcement Learning for Bi-directional EV charging: Today's EVs are mostly one-way, that is, they charge, but they cannot supply energy back to the grid. However, vehicle-to-grid charging is far more rewarding for home owners, especially those with their own solar panels. But when exactly should the EV be charged and when should it be discharged? This is a complex problem that is determined by when the EV is present at home, the next day's travel plans, grid requirements, and so on. The goal of this project is to use reinforcement learning to come up with EV charge/discharge control, similar to the approach proposed here, but extending the use cases to make it more realistic.
- Trunk diameter detection for complex trunks: In recent work we have used mobile phones with LIDAR to measure trunk diameters. However, our algorithm only works for nearly cylindrical trunks. The goal here would be to build on this work to tackle more complex trunks, such as those with burls, lianas, or multiple trunks.
- A solar PV layout and sizing tool: Residential solar PV is rapidly being deployed on rooftops around the world. In prior work we have studied the sizing of solar and storage (i.e. how many panels and how much storage to purchase) to meet a certain level of grid independence. This work, however, does not take into account the realities of rooftop PV, which includes limitations on where panels can be placed (avoiding skylights, for example), shadowing from rooftop protuberances and nearby trees, and self-shadowing from adjacent panels. The goal of this project is to use rooftop images, either from a satellite or a drone, to create a 3D model of the rooftop, then do an optimal placement, subject to a sizing requirement.
- Trusted image capture: The goal of this project is to link image capture from trusted hardware devices, such as Azure Sphere, to a global file store, such as IPFS, with summaries posted to a blockchain. This would allow us to trace an image to its creator with an unbroken chain of trust. Students who have some background working with microcontroller-based single-board devices, such as a Raspberry Pi, would be preferred. An alternative would be to develop the solution using an integrated hardware/software platform, such as iOS, and use iOS APIs to prove that the captured image, its time stamp, and device orientation had not been tampered with.
- Digital ID for carbon credit projects: In the context of carbon credit projects, we would like to provide participants with a digital ID that allows them to be paid for their projects, upload crowdsensed data about the project, and for those affected by the project to complain about unanticipated side effects. The digital ID will have to, therefore, be trustworthy enough for payments, yet provide protection to whistleblowers. How do we get participants IDs? What foundational ID do they need to begin with? The goal of this project is to design and implement a suitable solution, building on work such as this.