Power Aware

Status: 
ongoing
Period: 
March 2017 - March 2018
Funding: 
50.000 USD
Funding organization: 

Siebel Energy Institute

Person(s) in charge: 
Executive summary: 

Power Aware is a research program that won the 2016 Siebel Energy Grants and the 2013 MIUR (Ministery of Italian Education) Smart City and Social Innovation competition.
It concerns the use of collaborative web platforms to enhance citizens’ awareness on key factors of energy costs.

Background: 

Cutting down emissions caused by energy consumption is a priority for the European States that settled the ambitious goal to reduce energy usage of 20% by the year 2020. Power Aware aims at contributing to this goal by better educating the citizens and changing their behaviour.

Objectives: 

Power Aware will be a web platform comparing energy consumption patterns for citizens with similar characteristics (e.g., house size, family composition, number and type of appliances) and recommending saving strategies to reduce power consumption at homes. The comparison is made by means of interactive data visualisations that show to the citizens their power consumptions related to clusters of similar users. The web platform will offer additional features aimed at building a smart community, such as social tools and consumptions forecasting.

The mission of Power Aware is to enhance citizens’ awareness on key factors of energy costs, to reduce energy use by changing the consumption habits.

The research activities of Power Aware are conducted in collaboration with Midori, an innovative start up operating since 2012 in the field of energy analytics.

Results: 

The project is in the set-up phase. The expected results are:

  • D1 A list of parameters for users profiling and clustering and their data type requirements
  • D2 A list of data privacy issues (according to European laws) and countermeasures needed
  • D3 A model for the classification of clustering and machine learning algorithms
  • D4 A classification of clustering algorithms from literature according to D3
  • D5 Evaluation of the algorithms
  • D6 Elaboration of a visualization for users comparison and power forecasting
  • D7 Evaluation of the evaluation with users

midori


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