Difference between revisions of "Projectlist"
 
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=Part II/ACS projects (2022)=
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=Part II/ACS projects (2024)=
  
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* ''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, [https://www.canarymedia.com/articles/ev-charging/is-vehicle-to-everything-charging-ready-for-prime-time 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 [https://ieeexplore.ieee.org/abstract/document/9345625 here], but extending the use cases to make it more realistic.
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==Heatwaves==
  
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* ''Trunk diameter detection for complex trunks'': In recent [https://svr-sk818-web.cl.cam.ac.uk/keshav/wiki/images/0/01/Mobisys21postersdemos-final85_copy.pdf 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.  
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Climate change will result in more, more intense, and longer lasting heatwaves. To quantify the impact of heatwaves on building occupants, we have created a new metric called [https://www.dropbox.com/scl/fi/je68vqajyjk4bevbjlk6x/ActivityHoursDraft.pdf?rlkey=dyvfipst72xjhpv0njjxe2fmp&st=dc370bon&dl=0 Activity Hours] and the [https://www.cambridge.org/engage/coe/article-details/65ccc858e9ebbb4db958f3e9 Heatalyzer tool] (code released on [https://github.com/lcapol/Heatalyzer/blob/main/README.md GitHub]). I'm keen to supervise projects that build on this work. Examples include:
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* Integrating more archetypes and geographies into Heatalyzer, which currently only supports London weather and UK building archetypes.
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* Understanding the weather and building conditions lead to reaching liveability and survivability limits.
 +
* Analysing the sensitivity of results to building orientation and ventilation (which need to be modelled in Energy+)
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* Creating a map to show how the effects of a heatwave will be felt in each area of a city. For example, using socioeconomic data (available in the US on a congressional district scale) to map areas that will be affected by heatwaves in terms of their socioeconomic status and developing an interactive website/app to showcase results and aid engagement.
 +
* Modeling mitigation strategies in Energy+ and adding to the dashboard tool so that citizens can evaluate the impact of each strategy. Mark out strategies that do and do not depend on power grid availablity.
 +
* Exploiting local energy storage and energy use flexibility to mitigate the impact of a heatwave on the power grid using local energy.
 +
* Determining and representing uncertainty in our results due to lack of knowledge of building construction, weather, occpuant health status, acclimatisation effects and behaviour changes.
 +
* Personalization of our results to actual building data, matching the building data to the closest archetype and using all sources of information about the building, such as databases of building stock (e.g. US has 500K building database of representative buildings from NREL).
  
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*''A solar PV layout and sizing tool'': Residential solar PV is rapidly being deployed on rooftops around the world. In prior [https://svr-sk818-web.cl.cam.ac.uk/keshav/wiki/images/8/8b/Sizing_multiple_roofs-5_copy.pdf 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 [https://www.researchgate.net/profile/Rawad-El-Kontar/publication/344453369_Optimal_Efficiency_and_Operational_Cost_Savings_A_Framework_for_Automated_Rooftop_PV_Placement/links/5f7740cc299bf1b53e09526e/Optimal-Efficiency-and-Operational-Cost-Savings-A-Framework-for-Automated-Rooftop-PV-Placement.pdf optimal placement], subject to a sizing requirement.
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The outcomes I hope we can achieve using this work are
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* Identifying the specific parts of a population likely to be most affected by a heatwave and giving them advance warning on effective measures they should take to mitigate its impacts.
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* Giving policy makers the data they need to allocate funding to mitigation activities such as better insulation, exterior shading, and solar-powered air conditioning.
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* Alerting health care facilities to the expected number of heatstroke cases to expect from a heatwave.
  
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* ''Trusted image capture'': The goal of this project is to link image capture from trusted hardware devices, such as [https://ieeexplore.ieee.org/document/9302967 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.
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This is interdisciplinary work that brings together computer scientists with building simulation researchers in civil engineering, climatologists who forecast future climate, urban planners, policy makes, public health officials, and both local and central levels of government.
  
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* ''Digital ID for carbon credit projects'': In the context of [https://4c.cst.cam.ac.uk 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 [https://arxiv.org/pdf/2112.05566.pdf this].
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==Solar panel soiling==
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Solar panels can lose a considerable amount of their capacity due to soiling, thus panels have to be periodically cleaned. This comes at a cost so it is not economical to clean a panel array just before or just after a rainy day. What is the best time to clean a panel array (which can consists of hundreds of thousands of panels) and which panels need the most cleaning? In recent work, my Master's student Abhinav Bora used data from a large-scale installation to come up with some [https://uwspace.uwaterloo.ca/handle/10012/19293 answers]. This project would apply these to local installations at Cambridge, such as on the William Gates Building, or in Fitzwilliam College. If there is interest, we can also explore partnering with utility scale solar developers to analyse their plants. The goal would be set up a data acquisition system, do the data analysis, and provide actionable intelligence to utility scale solar plant operators.
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==Optimally placing solar panels on a flat roof==
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Optimally placing solar panels on a flat roof, parts of which can be shaded by chimneys, vents, nearby trees, and nearby roofs is a [https://www.spiritenergy.co.uk/kb-flat-roof-solar-mounting complex] problem. The goal of this project is to use a drone-based survey to create a 3D model of a rooftop and its vicinity, then uses ray tracing and either classical optimisation or DNNs to create an optimal layout to maximize energy production over the course of a year.

Latest revision as of 15:56, 12 July 2024

Part II/ACS projects (2024)

Heatwaves

Climate change will result in more, more intense, and longer lasting heatwaves. To quantify the impact of heatwaves on building occupants, we have created a new metric called Activity Hours and the Heatalyzer tool (code released on GitHub). I'm keen to supervise projects that build on this work. Examples include:

  • Integrating more archetypes and geographies into Heatalyzer, which currently only supports London weather and UK building archetypes.
  • Understanding the weather and building conditions lead to reaching liveability and survivability limits.
  • Analysing the sensitivity of results to building orientation and ventilation (which need to be modelled in Energy+)
  • Creating a map to show how the effects of a heatwave will be felt in each area of a city. For example, using socioeconomic data (available in the US on a congressional district scale) to map areas that will be affected by heatwaves in terms of their socioeconomic status and developing an interactive website/app to showcase results and aid engagement.
  • Modeling mitigation strategies in Energy+ and adding to the dashboard tool so that citizens can evaluate the impact of each strategy. Mark out strategies that do and do not depend on power grid availablity.
  • Exploiting local energy storage and energy use flexibility to mitigate the impact of a heatwave on the power grid using local energy.
  • Determining and representing uncertainty in our results due to lack of knowledge of building construction, weather, occpuant health status, acclimatisation effects and behaviour changes.
  • Personalization of our results to actual building data, matching the building data to the closest archetype and using all sources of information about the building, such as databases of building stock (e.g. US has 500K building database of representative buildings from NREL).

The outcomes I hope we can achieve using this work are

  • Identifying the specific parts of a population likely to be most affected by a heatwave and giving them advance warning on effective measures they should take to mitigate its impacts.
  • Giving policy makers the data they need to allocate funding to mitigation activities such as better insulation, exterior shading, and solar-powered air conditioning.
  • Alerting health care facilities to the expected number of heatstroke cases to expect from a heatwave.

This is interdisciplinary work that brings together computer scientists with building simulation researchers in civil engineering, climatologists who forecast future climate, urban planners, policy makes, public health officials, and both local and central levels of government.

Solar panel soiling

Solar panels can lose a considerable amount of their capacity due to soiling, thus panels have to be periodically cleaned. This comes at a cost so it is not economical to clean a panel array just before or just after a rainy day. What is the best time to clean a panel array (which can consists of hundreds of thousands of panels) and which panels need the most cleaning? In recent work, my Master's student Abhinav Bora used data from a large-scale installation to come up with some answers. This project would apply these to local installations at Cambridge, such as on the William Gates Building, or in Fitzwilliam College. If there is interest, we can also explore partnering with utility scale solar developers to analyse their plants. The goal would be set up a data acquisition system, do the data analysis, and provide actionable intelligence to utility scale solar plant operators.

Optimally placing solar panels on a flat roof

Optimally placing solar panels on a flat roof, parts of which can be shaded by chimneys, vents, nearby trees, and nearby roofs is a complex problem. The goal of this project is to use a drone-based survey to create a 3D model of a rooftop and its vicinity, then uses ray tracing and either classical optimisation or DNNs to create an optimal layout to maximize energy production over the course of a year.

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