1 research outputs found
Activity-Based Recommendations for Demand Response in Smart Sustainable Buildings
The energy consumption of private households amounts to approximately 30% of
the total global energy consumption, causing a large share of the CO2 emissions
through energy production. An intelligent demand response via load shifting
increases the energy efficiency of residential buildings by nudging residents
to change their energy consumption behavior. This paper introduces an activity
prediction-based framework for the utility-based context-aware multi-agent
recommendation system that generates an activity shifting schedule for a
24-hour time horizon to either focus on CO2 emissions or energy cost savings.
In particular, we design and implement an Activity Agent that uses hourly
energy consumption data. It does not require further sensorial data or activity
labels which reduces implementation costs and the need for extensive user
input. Moreover, the system enhances the utility option of saving energy costs
by saving CO2 emissions and provides the possibility to focus on both
dimensions. The empirical results show that while setting the focus on CO2
emissions savings, the system provides an average of 12% of emissions savings
and 7% of cost savings. When focusing on energy cost savings, 20% of energy
costs and 6% of emissions savings are possible for the studied households in
case of accepting all recommendations. Recommending an activity schedule, the
system uses the same terms residents describe their domestic life. Therefore,
recommendations can be more easily integrated into daily life supporting the
acceptance of the system in a long-term perspective