Household Electrical Consumptions Modeling and Management Through Neural Networks and Fuzzy Logic Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 457

8 Conclusions

In recent years (2005 2013), the whole Europe has highlighted a rapid growth of

the photovoltaic sector, after

-

the introduction of economical incentives by

governments.
In this context, due to higher feed in tariffs, the expansion involved mainly PV
systems on buildings. This situation poses notable issues since demand tend to be
relatively low during peak power production periods. On the same time a new PV
installation needs aa accurate sizing to smooth the so called “ back- fl ow ” effect and

maximize economical bene ts to owners.

In this scenario the modeling of residential energy use and the planning of
energy management actions can play a crucial role. Indeed the matching of the
production and consumption patterns is the only way to achieve satisfying eco-
nomical bene

ts.
This chapter deals with the description of a novel Fuzzy approach to model
household electrical consumption. The model is built using a approach

and the basic block is the single appliance. Using as inputs patterns of active
occupancy (i.e. when people are at home and awake) and typical domestic habits (i.
e. start frequency of some appliances), the FIS model give as output the starting
probability of each appliance. To validate the model we have recorded electricity
demand within 12 dwellings in Ripatransone (AP), in the central east coast of Italy,
over the period of 12 months. Simulation performances, in particular for what
regards daytime period (the mean error is 0.52 %), make possible its use for self
consumption estimation and PV sizing.
Energy management problem has been introduced and a neural network based
algorithm to forecasts of both photovoltaic production and home consumptions
presented. The considered algorithm, based on the minimal resource allocating
networks method, is used to perform long range predictions. In particular the power
production and home consumptions presented in the above tests is forecasted up to
24 h ahead. The proposed algorithm performs an on-line prediction and no previous
measures of PV plant

“ bottom-up ”

s production or electrical consumptions are needed. Therefore
the algorithm have been proposed with a pre-trained net based only on few his-
torical informations found on the web.
A case study on a possible use of the fuzzy tool has been presented. Starting
from the simulated consumption of a dwelling, a residential photovoltaic (PV) plant
has been sized according to a cost bene

'

ts analysis (CBA) in the new Italian
scenario. Net present value (NPV) and internal rate of return (IRR) have been
computed for different PV plant sizes. The obtained results show that the NPV
difference between the best and worst case can be 140 % (which results in more
than 1,200 ts of energy

management actions (shifting of the two main appliances) has been performed. The
CBA analysis shows that revenues can further increase from 250 to 600

). Furthermore a parallel analysis of the economical bene

€

€
(depending on the plant size) thus imposing cost limitation for the EM equipment.