Optimization of In-House Energy Demand


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 occupancy 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 appliance 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.

author	= {Stephan Spiegel},
editor	= {Frank Hopfgartner},
title	= {Smart Information Services - Computational Intelligence for Real-Life Applications},
chapter	= {Optimization of In-House Energy Demand},
series	= {Advances in Computer Vision and Pattern Recognition},
publisher  = {Springer International Publishing Switzerland},
volume	= {},
number	= {},
pages	= {19},
page	= {271-289},
month	= {March},
year	= {2015},
isbn	= {978-3-319-14177-0},
doi	= {10.1007/978-3-319-14178-7_10},
url	= {http://www.springer.com/computer/information+systems+and+applications/book/978-3-319-14177-0}
Stephan Spiegel
Smart Information Services - Computational Intelligence for Real-Life Applications, Springer