Household Electrical Consumptions Modeling and Management Through Neural Networks and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 435

frequency of the appliance, the time of the day, the temperature. For example, when
people are not at home, most appliances will not be used (only the so called
continuous use appliances).
In daily appliance electricity pro

le, the occupants use virtually little power
(stand by and fridge-freezer) during the night, may wake up and have breakfast,
vacate the house during the morning and then return around mid-day for lunch, e.g.
starting the microwave. In the evening, the meal is cooked, television is watched,
lights are on, showers are taken, etc.
This typical pattern can drastically change during the weekend and holidays
(when people can be in the house mostly during daytime) and, moreover, it can
change from dwelling to dwelling due to different life styles. The main factors
in uencing occupancy pattern and appliances usage are: the number of occupants,

the time the

fl

first person gets up in the morning and last person goes to sleep, the
periods house is unoccupied during work days, holidays and weekends. When
analyzing the households load pro le we need information on the active occupants

of the dwelling. To compute the overall occupancy pattern a speci ed model can be

used, for instance that one developed by Richardson et al. ( 2008 ).
Starting from basic information in this chapter we build a 1 minute resolution
daily active occupants pattern for each day of the week. To compute the number of
the busy occupants a counter is used; this counter is increased every time an
appliance that requires interaction with a person is switched on, and decreased
every time it is switched off. The number of unoccupied people in the dwelling can
be computed from the active occupants pattern and the current value of the busy
occupants counter. Knowing this value for each time of the day, we can enable or
interdict the switching on of the appliance.
A further important feature is to identify the typical frequency of each appli-
ance

' is starting for each household. This parameter is rarely a crisp value, e.g. “ the

washing machine starts usually from 2 to 3 times a week ” , and often related to the

time of the day, e.g. . In this

work all information regarding occupancy, appliances frequency and typical start
time are taken with a brief interview. The former are used to build the active
occupancy pattern and the latter to build fuzzy rules.

“ the television starts some hours a day usually at night ”

4 Appliances Fuzzy Inference System

The electricity consumption pattern model for any individual domestic dwelling is
developed using a approach, according to those proposed by Rich-

ardson et al. ( 2010 ). The basic block is the appliance, i.e. any individual domestic
electric load. As it is well known, home appliances differ one from each other by
size, functions, human interaction level, automation level.

“ bottom-up ”