Household Electrical Consumptions Modeling and Management Through Neural Networks and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 441

factor (equal to the difference in hours between the average period of use of the
appliance and the time elapsed since the last start), as stated in Richardson et al.
( 2010 ). The result is then compared with a random number (within the real interval
0

- 1). The appliance will start if:

this number is less than the scaled probability

￿

there is at least one person in the house

￿

there are suf cient active people in the house (only for some appliance ' s

￿

categories)

the sum between the current electrical consumption and the max power of the
appliance is less than the power the customer can absorb from the grid.

￿

Table 1 shows the need of taking into account also the number of active people
in the dwelling for

“ Periodical use appliances with human interaction ” and “ Mul-

timedia Appliances . Starting from the typical pattern of people in the household

we decrement this number when an appliance of one of these categories starts and
increment this number when the appliance is turned off.
To simulate EM actions, fuzzy rules have been modi

”

ed to approximate a
different user behavior regarding the starting time of the two main shiftable
appliances (dishwasher and washing machine). As an example, without any action,
fuzzy input sets for

“ ”

periodical use appliances without human interaction are:

the time of the day h(t)

￿

the time elapsed since the last appliance start multiplied his typical start fre-
quency DT/T(t)

￿

and a typical rule formulation is:
if h(t) is afternoon and DT/T(t) is late, then the probability to start the appliance
is low.
In the following section we will describe tests performed to validate the model.

5 Model Validation

We validated the model collecting a set of consumption data from 12 volunteer
dwellings in and around the town of Ripatransone in the province of Ascoli Piceno,
Italy. All people in these households have been brie

y interviewed to build
occupancy patterns and fuzzy rule sets starting from their typical energy habits.
A set of data loggers were installed in the dwellings and con

fl

gured to record

demand at 1 min intervals. An example 24 h demand pro le for a single dwelling

taken from the measured data set is shown in Fig. 6 . In order to create a con-
sumption database we installed in four of these dwellings individual appliance
monitors (IAMs from Current Cost company) to extract 6 s resolution consumption
data of every household monitorable load (e.g. washing machine, dishwasher,
multimedia appliances, iron, oven, microwave). For the remaining 8 dwellings,
appliances were not directly monitored, but the pro

les were used choosing for