Available technologies, Clean Technologies, Electrical and Electronic components, Energy, Engineering, Heating, ventilating, and air conditioning (HVAC)

JOINT ELECTRICITY PREDICTOR AND CONTROLLER

Smart method and system for better predict electricity grid activities, and then better plan energy production, management and distribution

The challenge in forecasting accurate utility consumption      

  • Forecasting accurate utility consumption such as electricity, gas, and water or waste generation is a challenge.
  • However, failure to predict accurately can lead to insufficient supply and penalties for excess consumption / generation.
  • Consider electricity for example, in order to perform this task, electricity suppliers today are using aggregate prediction due to the lack of information about individual electricity usage and behavior.
  • While if they could predict energy consumption at the scale of the individual service subscriber, they would better manage their electricity grid, optimize their energy production (by using more renewable energy for example) and improve its distribution.
  • Accordingly, it would be beneficial to provide a system that can jointly predict a consumer’s energy consumption, and at the same time provide management.
  • By adjusting wisely the consumer’s appliances and its Heating Ventilation and Air Conditioning (HVAC), the system can maximize the fit with the performed prediction, where significant deviations from plan or consumptions above certain thresholds lead to increased costs.
  • With such a system, the predicted individual usage details can be transmitted to the energy supplier as a commitment in a structured format that can be processed automatically.

New design/method for joint electricity predictor

  • Joint electricity predictor and controller is a system that allows energy suppliers to better predict their electricity grid activities, and then better plan energy production, management and distribution.
  • It consists of a smart electronic device that collects information about individual energy consumption, learning its routine and exceptions in order to predict its future energy consumption.
  • The smart thermostat produces an energy consumption commitment based on its user consumption prediction. It then adjusts the HVAC appliances in order to respect the prediction and sends the prediction content to the energy provider, who can later reward the user if the prediction was respected.
  • Technology developed by Jia Yuan Yu, and Mehdi Merai (Institute of Information System Engineering, Concordia University)

Competitive advantages

  • Forecasting electricity consumption combined with the control of appliances in order to respect forecast is an advantage for BOTH the energy provider and the user.
  • Device could contribute to the emergence of a new electricity economy where the energy suppliers can offer rewards to their users based on their respect of the prediction provided

Market applications 

  • Prediction, control and management of electricity consumption, gas, water or waste generation, combined with a reward system.
  • Any utility consumption can be managed

Business opportunity

  • Technology available for licensing/Partnering/ Co-development
  • WO18098562
  • CA3045519

CONTACT

If you are interested by this technology, please contact :
Pierre des Lierres, Director Business Development, Engineering
PdesLierres@aligo.ca, (514) 571-6556

UNIVERSITY

Concordia University

Main inventors

jia

Jia Yuan Yu, Professor, Institute of Information System Engineering

Prof. Jia Yuan Yu is an associate professor in the Concordia Institute of Information Systems Engineering. His work applies data science and decision theory to smart cities, Internet-connected devices, and other multi-agent systems. He has previously worked at IBM Research, the Dublin City University, the Ecole Normale Superieure Paris, Stanford University, and Intel Research. His research has been funded by the European Commission under the FP7 and H2020 programs and by IBM. Jia Yuan obtained his PhD in electrical engineering from McGill University in 2010