Abstract: |
Improving building standards and facility services in residential buildings is one major effort for future energy
savings. Due to current facility standards and tightened legal restrictions, automated air ventilation (AVS)
can contribute large potentials towards energy consumption downsizing. Yet another savings potential can be
achieved by providing homogenous media allocation in central heating systems. (Szendrei, 2010) One major
effort for energetic optimisation is seen in the integration of AVS and space heating systems in building automation
frameworks. As heating losses by window airing refer to faulty user behavior, the parameters: room
temperature Jr and indoor air quality (IAQ), expressed by CO2-concentration and relative humidity, are possible
subjects for building automation. Building-automation bus systems ensure a holistic energy management
and control while maintaining thermal comfort at high level. Since the interferences between space heating,
air ventilation and building physics are highly complex, integrative support- and management systems are
required. In this paper the effort of transferring heat from intermediate high temperature level zones (IHTL),
such as bathrooms and kitchens into long term medium temperature level zones (LMTL) by using air ventilation
systems with heat recovery is presented (D. Szendrei and Worms, 2011). Furthermore the implications
of hydraulic homogenous mass flows regarding heat energy savings are named as examples for support- and
management system design. After naming all relevant energetic parameters, the design of Artificial Neural
Networks (ANN) with respect to the presented energetic application is presented. In section 3 we present all
relevant input data, required for energetic optimisation, and the basic neural algorithm. As an example, the
automatic adjustment of set temperatures for space heating in residential buildings is described. |