TAIZ is a non-intrusive load monitoring and energy disaggregation platform that is attached to normal household energy meters, measuring real-time appliance-level data on energy consumption patterns. It goes beyond what most electricity meters today provide, by presenting accurate and actionable itemized data to both consumers and utility providers. We accomplish this through machine learning. The information is delivered to the user on mobile or web. The solution will enable consumers to monitor their energy needs in real time, understand their consumption patterns, and then optimize their energy usage. Itemizing energy usage would result in economic savings in electricity bills for end consumers, while the aggregate data of consumption patterns would help utilities their services during peak hours.
When widely adopted, it would lower the energy footprint of the ecosystem and, as a result, generate massive savings. The device would, in effect, function as a smart meter. It will be easily manufacturable, as are most smart meters, consisting of easily available processing chips and micro-controllers.
The device will represent a massive step forward in the way consumers view energy consumption, shifting from a bulk consumption model with opaque pricing, to an itemized bill with valuable information on reducing energy consumption.
ABOUT THE ENTRANT
Name: Adnan Ul Haq Babar
Type of entry: team
Taha Rizvi - National University of Sciences and Technology, Pakistan Adnan Ul Haq Babar - National University of Sciences and Technology, Pakistan Jamshaid Zafar - National University of Sciences and Technology, Pakistan Ibtehaj Khan - National University of Sciences and Technology, Pakistan
Adnan Ul Haq is inspired by:
The team is inspired by the need to reduce human footprint on the ecosystem. Any step that lowers our energy requirements is a step towards a greener future.
Patent status: none