Projectlist

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.

Integrated Sizing, Operation, and Placement Optimisation of Solar Panels on Multiple Roof Segments

The deployment of rooftop solar panels is growing rapidly. However, the high initial capital cost remains a significant barrier for many homeowners. Additionally, solar panels that are not optimally placed may fail to achieve their maximum generation potential and, in some cases, may generate no energy at all due to issues such as self-shading. This project aims to enhance an existing framework for the joint sizing and operation of solar panels by incorporating a placement optimisation tool. An extension would be to create precise 3D models of houses and their surroundings using drone-based Lidar data, enabling accurate modeling of shading and complex roof structures.

Joint Sizing and Operation of PV/EV/Heat Pumps in Residential Homes

The transition from gas furnaces to heat pumps is accelerating, and an increasing number of homeowners are seeking to electrify their homes with solar PV, storage systems, and electric vehicles (EVs). The objective of this project is to develop an integrated algorithm for the optimal sizing and operation of heat pumps, considering the presence of solar PV, storage solutions, and EVs.

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, assuming the availability a 3D model of a rooftop and its vicinity, using ray tracing and either classical optimisation or DNNs to create an optimal layout to maximize energy production over the course of a year.