Responsive Algorithmic Enterprise (RAE)
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Description
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.
| AND TECHNOLOGY RESEARCH LIMITED | LEAD_ORG |
| UNIVERSITY OF READING | PARTICIPANT_ORG |
| AND TECHNOLOGY RESEARCH LIMITED | PARTICIPANT_ORG |
Subjects by relevance
- Algorithms
- Optimisation
- Energy technology
- Energy consumption (energy technology)
- Energy
- Simulation
Extracted key phrases
- Responsive Algorithmic Enterprise
- Energy datum analytic
- Energy datum available
- Predictive control algorithm
- Energy control
- Energy monitoring equipment
- New algorithm
- Energy management
- Appliance signature datum
- Technology Research
- Project
- RAE
- Meso level
- Peak load
- Plastic factory