Responsive Algorithmic Enterprise (RAE)

This project aims to develop new algorithms for energy control utilising appliance signature data from three

case-studies (a car-park, a plastic factory and a village community). The algorithms will be used for peak load

management and load balancing and will be designed to rectify previously identified issues in monitored data

due when simple demand response is applied. In addition the algorithms will be able to take advantage of

secondary power sources such as PV, wind and battery storage. This is a collaboration between the University

of Reading using their expertise in energy data analytics and optimisation and AND Technology Research with

the expertise in energy monitoring and control. Trials will then be undertaken through simulation and tested

using energy monitoring equipment that has been pioneered by AND Technology Research. This equipment is

designed for cost effectiveness and is targeted at the organisations operating at a meso level. In summary, the

University of Reading and AND Technology Research will develop predictive control algorithms for meso-level

energy management based on the energy data available from monitoring.