Household Electrical Consumptions Modeling and Management Through Neural Networks and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 433

2 Related Work

The analysis and identi cation of energy consumption pattern nowadays is

receiving strong interest together with fault diagnosis of appliances components and
there have been a large number of researches in this area (Ferracuti et al. 2013a , b ;
Ihbal et al. 2011 ; Zaidi et al. 2010 ; Zia et al. 2011 ).
Another related research '

electricity consumption patterns, see, e.g., Azadeh et al. ( 2008 ), Barbato et al.
( 2011 ), Ciabattoni et al. ( 2013c ), Gruber and Prodanovic ( 2012 ), Murata and Onoda
( 2002 ), Osman et al. ( 2009 ), Subbiah et al. ( 2013 ). Most of the existing models and
analysis focus on data from speci

field regards the forecast and simulation of households

c geographic regions and try to explain the
results in a local perspective (Guo et al. 2011 ; Suh et al. 2012 ).
Photovoltaic sizing is an important research

field in this area but most of the
works concern with the optimization of stand alone systems without an analysis of
the demand response scenario for grid connected users, see e.g. Benghanem and
Mellit ( 2010 ), Ciabattoni et al. ( 2013a ), Jakhrani et al. ( 2012 ), Jallouli and Krichen
( 2012 ), Kaabeche et al. ( 2011 ). In this scenario only the knowledge of the typical
demand pattern for each household will make possible the proper sizing of a
photovoltaic plant, the design of demand response techniques and energy man-
agement actions. 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.
Energy usage models developed in literature e.g. in Bernard et al. ( 2011 ),

Richardson et al. ( 2010 ) are con gured using statistics describing mean total annual

energy demand and associated power use characteristics of household appliances.
Furthermore these modeling approaches (Bernard et al. 2011 ; Richardson et al.
2010 ; Widen et al. 2009 ) concern speci c household energetic behavior without an

easy customization capability. It is often impossible to add every appliance and
predispose a

(Bernard et al. 2011 ), (Richardson et al. 2010 )
without using the flexibility of a fuzzy inference systems, as proposed in this work.
It is well known that overall cost-saving by distributed generation would only
have a marginal impact if the demand pattern does not match with the production
one and no actions of energy management are performed. In this scenario only the
knowledge of the typical demand pattern and the forecast of the generation pattern
for each household will make possible the design of proper demand response
techniques and the planning of energy management actions. In this context energy
management for residential consumers has become a signi

“ seasonal behavior ”

cant research and

development field for both electrical (Ciabattoni et al. 2013d ) and thermal side

(Giantomassi et al. 2014a , b ), as a result of the advances in the electrical power grid
technologies and the high penetration of solar, wind and other forms of Distributed
Generation (DG) (Ciabattoni et al. 2012 , 2013e ; Cimini et al. 2013 ; Kanchev et al.
2011 ). Less attractive feed-in-tariffs for new installations of renewable energy DGs
(solar, wind and geothermal plants) and incentives to promote self-consumption
suggest that new operation modes should be explored in order to reach grid parity,