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which has been predicted to become a reality in the next years in the European
Union (Fazeli et al.
2011
; Kanchev et al.
2011
; Palensky and Dietrich
2011
; Zong
et al.
2012
). By increasing the self consumed local generated energy, the grid parity
could be achieved earlier and DG of renewable energies will
finally make economic
sense becoming cheaper (over the lifetime of the system) than to buy it from utility
(Aghaei and Alizadeh
2013
; Lewis
2009
; Lopez-Polo et al.
2012
).
There are increasing numbers of studies on smart homes and the bene
ts of
demand-side management (Di Giorgio et al.
2011
; Shahgoshtasbi and Jamshidi
2011
; Zeilinger
2011
) and control and monitoring techniques to reduce overall
energy usage Meyers et al. (
2010
).
3 Fuzzy Inference System
Fuzzy rule-based systems (FRBS) have been successfully employed for system
modeling in many areas Azar (
2010b
). Existing fuzzy systems in the literature Azar
(
2010a
,
2012
) can be classi
ed into three main categories: Mamdani, Takagi-Su-
geno (T-S) and Tsukamoto systems based on their implemented fuzzy rule struc-
tures. Furthermore, depending on the intended application, the fuzzy modeling
research field can be divided into two main approaches.
The
first is the linguistic fuzzy modeling (LFM) where good human interpret-
ability of the underlying fuzzy model is paramount for tasks such as knowledge
mining and data analysis. This is usually achieved by adopting the Mamdani rule
structure for knowledge representation.
The other is the precise fuzzy modeling (PFM) where T-S and Tsukamoto fuzzy
rule structures are generally used in the learned fuzzy model to achieve high output
accuracies for function approximation and regression-centric applications. Having
good fuzzy rule-base interpretability and high modeling accuracy are contradictory
requirements and one usually prevails over the other based on the modeling
objective and fuzzy rule structure employed.
Generally, Mamdani fuzzy models are more interpretative than T-S fuzzy
models from a human perspective and thus can better explain and describe a
modeled system
'
s behaviors.
3.1 Fuzzy Modeling
'
The modeling of the appliance
s usage has been performed with a LFM approach to
determine if wether or not it is going to be started. Since the aim of this work is to
represent the household energetic behavior we choose Mamdani model, in order to
give the best interpretability to the rules.
The usage pattern, depending on the appliance
s category, can be related to
many variables, such as the number of active people in the house, the typical
'
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