Information Technology Reference
In-Depth Information
Chapter 10
Optimization of In-House Energy Demand
Stephan Spiegel
Abstract Heating control is of particular importance, since heating accounts for
the biggest amount of total residential energy consumption. Smart heating strategies
allow to reduce such energy consumption by automatically turning off the heating
when the occupants are sleeping or away from home. The present context or occu-
pancy state of a household can be deduced from the appliances that are currently in
use. In this chapter, we investigate energy disaggregation techniques to infer appli-
ance states from an aggregated energy signal measured by a smart meter. Since
most household devices have predictable energy consumption, we propose to use the
changes in aggregated energy consumption as features for the appliance/occupancy
state classification task. We evaluate our approach on real-life energy consumption
data from several households, compare the classification accuracy of various machine
learning techniques, and explain how to use the inferred appliance states to optimize
heating schedules.
Sustainable Energy: The Early Adopter Scenario
Steven and his wife Suzanne love to relax in their garden behind the house on warm
summer evenings. Usually, they enjoy dinner with a glass of wine on their garden
terrace, talking about the kids, Clara and Carl, or listing to the latest news from the
local radio station. Yesterday evening there was a radio broadcast about the advance
of smart meters and their potential to reduce the energy consumption in residential
homes. Suzannewas excited about the idea of saving energy by themselves, especially
since the power market has continuously raised prices over the last couple of years.
Steven has always been fond of Suzanne's commitment to sustainable living and
suggested to contact Ralph, an old schoolmate of him, who runs his own little business
in the IT sector and also is a trained electrician.
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