The goal is to automatically itemize the energy consumption down to the specific appliances and then provide specific, actionable recommendations to the consumers.
This application takes in the Green Button data that has your specific electric meter readings pattern over time and figures out automatically what each major appliance in your house consumes. The application does this by correlating specific energy signatures of specific appliances to the electric meter reading pattern over time. This technique is a new theory concept called disagreggation where an aggregate reading is decomposed to its sources through machine learning algorithms. The library of appliance consumption signatures can be developed statically over time as a library.
Once the itemized measurements (i.e. when each appliance was on and off) are generated then specific recommendations can be generated. For example:
- Recommend an alternate appliance that could lower consumption based on historical usage patterns.
- Recommend a different pricing approach (tier vs. peak)
- Recommend using specific appliances at different times of day based on usage pattern.
Team members: [email protected] [email protected] [email protected] [email protected]
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