Role: Creating ‘virtual assets’
GridDuck has created a wireless cloud-based energy efficiency and flexibility solution, with a dashboard, a RESTful API and a portfolio of wireless relays, controllers and sensors (bought in from 3rd party manufacturers).
Energy suppliers, consultants, facility managers and demand response aggregators can analyse and automate their client’s consumption at the individual appliance level.
This is invaluable for energy efficiency and energy flexibility schemes, such as time of use tariffs, local consumption balancing and implementing demand response.
Project: Helping us creating ‘virtual assets’, pools of similar appliances (e.g. 20 fridges or 20 air con units).
The growth in renewable energy and in electric vehicles means balancing the grid becomes paramount. Energy flexibility schemes (demand response, time of use tariffs, virtual power plants) need to be able to scale up from managing small numbers of large assets to managing large numbers of small assets.
But the individual energy consumption of these appliances is hard to predict, which means they cannot be planned in for energy flexibility schemes. As a pool their behaviour can be made more predictable.
Skills we need:
- Python experience, including Numpy & SciPy
- Interest in machine learning, ideally some experience with TensorFlow or similar
Location: London N19 4NF (unit 6)
How to apply: