Responsive Algorithmic Enterprise (RAE)

Stage: Round 4

This project develops algorithms for energy control using appliance signature data from case studies (carpark, plastic factory, village community). The algorithms address issues in monitored data when applying demand response, focusing on peak load management and load balancing. They also leverage secondary power sources like PV, wind, and battery storage.

The collaboration involves the University of Reading’s expertise in energy data analytics and optimisation, and AND Technology Research’s energy monitoring and control proficiency. Simulations and tests will be conducted using cost-effective energy monitoring equipment pioneered by AND Technology Research, focusing on meso-level organizations. The project aims to develop predictive control algorithms for meso-level energy management based on monitoring data.