Highlights


 Prototype Measurement System: To better understand how energy is generated and consumed in homes powered by renewable sources, we have deployed the first version of a measurement system in an off-grid home in Arkansas.  We collect the instantaneous residual battery voltage (in Volts) and the energy consumed by the house (in KWh).  We have also deployed networked, off-the-shelf WattsUp meters to measure power consumption of the television and refrigerator in the home.  Analysis of the data suggests the following:
  • Finding 1 - Traditional energy management techniques are insufficient: We first explore whether established norms of energy management, for example the 7PM-7AM policy that recommends users run important household appliances between 7PM and 7AM when the demand for power is low, are appropriate for green homes.  We find that, though energy consumption varies considerably over 24 hours, a large portion of energy is consumed between 10AM and 8PM.  Conversations with the home residents indicate that this consumption pattern is largely driven by the residual energy in the batteries; the residents try to use energy during the day when the most energy is being generated.  These findings demonstrate a need to develop new energy management techniques that consider the unique energy harvesting conditions in homes powered by renewable technologies.
  • Finding 2 - Energy generation and consumption is both variable and predictable: We next observe that both energy harvested from the panels and energy consumed by the house is highly variable, yet predictable. There is variance in generation and consumption across a single day, across several days, and across seasons. This suggests that fixed energy management strategies are insufficient and adapting to variability is a key element for green homes.  Moreover, we observe that there is considerable predictability in the data, pointing to the feasibility of automated and proactive energy management schemes.
 
 
 


Recommendation System and Smartphone Application: We are developing a recommendation-based system that uses information collected by the measurement system to provides users with advice regarding energy consumption, including warnings in advance of critical battery situations, recommendations for the best times to execute high-power tasks such as running a clothes dryer, and opportunities to adjust the power states of devices to reduce energy consumption. We have chosen a recommendation-based model to minimize user irritation and ensure that control of household appliances ultimately resides with the user. A smartphone application notifies the user when the system suggests changes to the power states of devices, for example suggests that the user turn an appliance on. The user is responsible for implementing the suggestion, and control of some devices is supported via the smartphone application.

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