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Annals of DAAAM for 2011 & Proceedings of the 22nd International DAAAM Symposium, Volume 22, No. 1, ISSN 1726-9679
ISBN 978-3-901509-83-4, Editor B. Katalinic, Published by DAAAM International, Vienna, Austria, EU, 2011
Make Harmony between Technology and Nature, and Your Mind will Fly Free as a Bird
Annals & Proceedings of DAAAM International 2011

NEURAL NETWORKS FOR SMART HOMES ANDENERGY-EFFICIENCY

TEICH, T[obias]; ROESSLER, F[alko]; SZENDREI, D[anny] & FRANKE, S[usan]

Abstract: In a project to reduce energy consumption, the use of
technology which helps metering and controlling lifestyle
effects is essential. Smart meters and intelligent systems that
contribute to environmental arwareness enable private
homeowners or tenants to see and actively control their costof
lifestyle. As a part of Smart Home systems neural networks are
considered to be of assistance for user-based systems and
consumption prediction. The observation of collected data over
a period of time offers many opportunities to disvocer potential
applications that help optimizing specific tasks. Controlling the
target temperature at a specific time of day, based on the habits
andpreferences of a tenant is one first chosen way to make
daily life easier and at the same time make it possible to design
Smart homes that compromise between energy-efficieny and
personal comfort. For that purpose a neural network is
designed and tested under varying premises. The results are
promising and the insights will enable future works in following
projects.
Key words: neural networks,learning, energy-efficiency,
neural control

1. INTRODUCTION
This paper contributes to a research project at UAS
Zwickau in collaboration with a communal housing association.
Various sensors in newly refurbished tenements capture data of
energy consumption at radiators and bus bars as well as air
quality, temperature and weather conditions (Teich et al.,
2010). At the present time tenantshave the possibility to adjust
the desired target temperature for every single room via a
central touch panel that can also be timed beforehand. These
settings have to be changed as specific conditions alter. There
are data which prove that settings for different time had been
changed once or even several times a day. The objective for the
current research is how to train and implementneural networks
that can assist users in setting temperature profiles. As one
natural adaptation the data can be used to train a neural network
on the tenant's preferences that then will automatically adjust
the target temperature regarding the data.

2. NEURAL NETWORKS
Biologically inspired procedures such as genetic
algorithms, ant colony optimization and neural networks
(Kramer, 2009)apply to areas where traditional mathematical
methods can not be realized due to the fact that there are not
enough resources to find an exact solution. These procedures
mimic the way of cooperation and information processing that
exists in nature. With (artificial) neural networks we have one
abstract method comparable to the processing capabilities of the
human brain. In practice they arebeing used as subsystems in
algorithmic frameworks since they are not suitable to provide
an overall system (Deco, Schuermann, 2001). Yet they can
perform complex tasks ranging from recognition to short-term
projection.
Neural networks consist of a minimum of two layers of neurons
which each has an input, a body and an output part. In case

there are only two layers, the first, also calledinput layer will
get the input directly from external data, so there is no input
connection from other neurons. A neuron will generate an
output level from its total activation, i.e. the sum of net input
which is then forwarded to the connected neurons in the next
layer. The output level may be kept the same value as input for
the following neurons, though a multiplication with a variable
is...