Datasets required

Explore all the datasets associated with each Master’s project idea and can be found here.

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EV Master’s project ideas

  • What are some of the properties of current location of chargepoints, what are some of the gaps?

    Open question to explore different attributes, but one focus could be on rural vs urban, or inequality.

    SENSE datasets required:

    1. ZapMap

    Other datasets:

    1. Demographic data

    2. Other

  • Create or utilise open algorithms to optimise the location of EV chargepoint based on various data, including current locations, capacity, etc.

    Possibly expand to look at socio-demographics and improve equity.

    SENSE datasets required:

    1. Zap Map

    Other datasets

    1. Capacity map (from DNO)

    2. Chargepoint coverage data from Field Dynamics

  • Consider the Zap Map data and when they were installed to create a long term scenario driven model to identify how many and where the installations may be given historical patterns.

    Link to other initiatives to see impact of policies, and try to understand impact of new initiatives like the LEVI fund. 

    SENSE datasets required:

    1. Zap Map

  • With possibility of autonomous buses and taxis. How will this effect the EV charging infrastructure needs and the users. Where should they be placed, and what impact may they have on different communities?

    SENSE datasets required:

    1. Zap Map,

    2. Synthetic Charging data

    3. Oxford Partnerships Mobile Network Data (and other movement data)

    Other datasets:

    1. Projects of Autonomous Vehicles

    2. Political Strategy

    3. Other

  • Modelling the use of EV charging in changing weather, can we also predict the use in various extreme weather scenarios.

    SENSE datasets required:

    1. Synthetic charging data

    2. Solihull charging data

    3. Scotland charging data

    Other datasets:

    1. Urban EV

    2. Weather data

  • Utilising the mobile phone data, what are the types of journeys it captures, which ones could be linked to different energy use behaviours.

    SENSE datasets required:

    1. Oxford Partnerships Mobile Network Data

  • There is clearly difference between domestic and non-domestic users of public charging infrastructure, but within these groups what are the different behaviours and what association do they have with different roles, and user types?

    SENSE datasets required:

    1. Public charging data from Solihull

    2. Scotland charging data

    3. Oxford Partnerships financial data

    Other datasets:

    1. Weather data

  • Based on charging patterns in home and public chargers, what are the opportunities and risks of using plugged in vehicles to manage grid constraints?

    SENSE datasets required:

    1. Mobile phone movement data

    2. Synthetic data

    3. Solihull charging data

    4. Scotland charging data

    Other datasets:

    1. Weather data

  • What are some of the innovative ways users of EVs can get value or reduce costs from using their EVs?

    How can these be applied so as to support, or at least not disadvantage vulnerable and/or fuel poor individuals?

    SENSE datasets required:

    1. Oxford Partnerships financial data

  • Rural areas are typically underserved with charging infrastructure, what are some of the differences in rural vs urban usage, in terms of location, how they are used, and what would be a better way to utilise?

    SENSE datasets required:

    1. Oxford Partnerships financial data

    2. Solihull public charging data

    3. Scottish EV charging dataset

    Other datasets:

    1. Weather data

  • Can be focused in different datasets, or multiple. But what are e.g. the ways to better visualise the usage of different chargepoints, EV journeys (possibly using Mobile data), EV placement, including different users, etc.

    How to tell story’s for decision makers?

    SENSE datasets required:

    1. Synthetic public charging data

    2. Living Lab data

    3. Mobile phone data

  • What do users do when they are charging their vehicles?

    Does this vary depending on location, type of charging, users?

    SENSE datasets required:

    1. Zap Map

    2. Mobile phone data

    Other datasets

    1. Demographic

    2. Census data

    3. Building types around the locations and their operational use

  • This project aims to understand how the use of public chargers link with how EV chargers are utilised at homes. Is there common patterns, what does it say about the users, and how may changes in use in one area affect the are in others?

    SENSE datasets required:

    1. Solihull public charging data

    2. Charge Scotland data

    3. Oxford Partnerships financial data

    Other datasets

    1. Urban EV

Non-domestic Master’s project ideas

  • Public buildings can offer valuable resources which may not be available to everyone, including heating and cooling services (e.g. in heat waves).

    This may be increasingly important as climate changes. What role can they play and how effective are they?

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Energy consumption

    Other datasets:

    1. Weather data

  • How is energy linked to movements within the building, this could ensure non-essential systems are shut down. How much can be inferred with non-mobile data, like door access keycards for example? 

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Mobile data

    3. Energy data

    Other datasets:

    1. Weather data

  • How can we build effective, affordable plans for decarbonising building energy usage?

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Energy consumption

    Other datasets:

    1. Weather data

  • Often occupancy behaviours is sparse for non-domestic buildings.

    SENSE datasets required:

    1. Campus datasets

    2. Mobile phone data

    3. NHS building energy data

    Other datasets:

    1. Weather data

  • Having appropriate heating and cooling within buildings reduces risks of health conditions. How can energy be managed whilst ensuring that comfort and health are not affected?

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Energy consumption

    Other datasets:

    1. Weather data

  • How are movement and presence patterns affecting heating, cooling, lighting and ventilation demand. Are there inefficiencies, what approaches could improve this?

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Energy consumption

    3. Mobile phone data

    Other datasets:

    1. Weather data

  • Measuring flexibility potential varies across energy profile and type of consumers. Metrics may simplify and help quantify potential to grid and business.

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Energy consumption

    Other datasets:

    1. Weather data

  • What are some of the differences and similarities between different building types, what about same building types (e.g. universities), and what accounts for these differences. Can we build a model?

    SENSE datasets required:

    1. Campus/NHS datasets

    Other datasets:

    1. Weather data

  • What are the areas of potential flexible demand based on the building type and their energy use data?

    Potentially could look at disaggregating energy demand to identify specific flexibility opportunities. 

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Energy consumption

    Other datasets:

    1. Weather data

  • What is the likely effect on buildings and how can they prepare for future climate change? 

    SENSE datasets required:

    1. Campus/NHS datasets

    2. Energy consumption

    Other datasets:

    1. Weather data

    2. Climate scenarios