Household Electrical Consumptions Modeling and Management Through Neural Networks and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 432

In this scenario the modeling of residential energy use and the planning of
energy management actions can play a crucial role (Ciabattoni et al. 2013b ). The
pattern of electricity use for any individual domestic dwelling is highly dependent
upon the activities of the occupants and their associated use of electrical appliances.
In this chapter we present a high-resolution model of domestic electricity use, based
upon a combination of patterns of active occupancy and daily activity pro les

(typical appliances usage frequency and starting time). The model is built using a
“

approach, according to Richardson et al. ( 2010 ). The basic building
block is the appliance, i.e. any individual domestic electric load. The model,
managing the start of each appliance in the household through a fuzzy logic
inference system, gives as output the 1 min resolution electricity usage pattern. All
data necessary to build the fuzzy inference system are obtained from 2 weeks of
measures through wireless smart plugs installed in the households appliances with
an automatic procedure. Fuzzy sets and rules are determined with an automatic
procedure analyzing sensors measures. In order to validate the model, electricity
demand was recorded over the period of a year within 12 dwellings in the central
east coast of Italy. A through quantitative comparison is made between the synthetic
and measured data sets, showing them to have similar statistical characteristics.
The problem of household energy management has been discussed and a pos-
sible solution presented through neural network based forecasts of consumption and
PV production used to inform and influence prosumers on the way they use elec-
tricity to increase the amount of self consumed energy.
The fuzzy model has been used for a case study on the proper sizing of a PV plant
(Benghanem and Mellit 2010 ; Jakhrani et al. 2012 ; Jallouli and Krichen 2012 ;
Kaabeche et al. 2011 ) in the central east region of Italy and the evaluation of Energy
Management potential bene

bottom-up ”

ts analysis (CBA). The
installation in a dwelling of all the devices necessary to actuate proper EMpolicies has
a relatively high cost compared to that of a PV system (Di Giorgio et al. 2012 ; Sawyer
et al. 2009 ). The focus of this analysis is to set an upper limit for the equipment cost in
order to obtain real savings for a speci

ts based on a costs bene

c household through the CBA.
The chapter is organized as follows. An overview of the related works appears in
the second section. A brief introduction on the fuzzy inference system modeling is
reported in the third section. The structure of the model, a human interaction based
classi

cation of the appliances into different categories, a sample of the rule set, the
National Instruments Labview software implementation details are reported in the
fourth one. Model validation results are given in Section

five, where the simulator
output is compared with one year data sets recorded from 12 dwellings in the
central east coast of Italy. In the sixth section energy management problem and
neural network based forecasting algorithms for both photovoltaic production and
home consumptions are described. In Section seven is presented the application of
the FIS consumption simulator for the PV optimal sizing and energy management
bene ts evaluation in a case study.