Environmental Engineering Reference
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Fig. 6.2
Application of the BAC factors method
(Ala et al. 2008 ; Campoccia et al. 2008 ; Capasso et al. 1994 ) share a probabilistic
approach allowing the construction of the daily profile starting from the knowl-
edge of social, economic and demographic elements. Several probability functions
cover the close relationship existing between the demand of residential customers
and the psychological and behavioural factors that are typical of the inhabitants of
the household; the models make use of such functions through a Monte Carlo
extraction process. Here, the daily power profile of the test house is found
according to the bottom-up approach defined in Ala et al. ( 2008 ) and imple-
mented in the tool SirSym-Home developed by the Department of Energy
Information technology and mathematical models of the University of Palermo,
Italy, DEIM, within the National Research Project SIRRCE ( 2010 ).
The tool allows the calculation of the yearly energy consumption starting from
the daily power profile of the building, influenced by:
• the number of inhabitants of the house;
• the period of the year (winter or summer season);
• the different
working cycles
of some
devices
(electric oven,
dishwasher,
washing machine, etc.).
Given the unpredictability of these factors, to proceed with the calculation of an
average daily profile it is necessary to implement a Monte Carlo approach. 2
2
Monte Carlo methods are a broad class of computational algorithms that rely on repeated
random sampling to obtain numerical results; typically one runs simulations many times over in
order to obtain the distribution of an unknown probabilistic entity.
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