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Household Electrical Consumptions
Modeling and Management Through
Neural Networks and Fuzzy Logic
Approaches
Lucio Ciabattoni, Massimo Grisostomi, Gianluca Ippoliti
and Sauro Longhi
Abstract In recent years the European Union and, moreover, Italy has seen a rapid
growth in the photovoltaic (PV) sector, following the introduction of the feed in
tariff schemes. In this scenario, the design of a new PV plant ensuring savings on
electricity bills is strongly related to household electricity consumption patterns.
This chapter presents a high-resolution model of domestic electricity use, based on
Fuzzy Logic Inference System. The model is built with a
approach and
the basic block is the single appliance. Using as inputs patterns of active occupancy
and typical domestic habits, the fuzzy model give as output the likelihood to start
each appliance within the next minute. In order to validate the model, electricity
demand was recorded over the period of one year within 12 dwellings in the central
east coast of Italy. A thorough quantitative comparison is made between the syn-
thetic and measured data sets, showing them to have similar statistical character-
istics. The focus of the second part of this work is to develop a neural networks
based energy management algorithm coupled with the fuzzy model to correctly size
a residential photovoltaic plant evaluating the economic benefits of energy man-
agement actions in a case study. A cost bene
“
bottom-up
”
its analysis is presented to quantify its
effectiveness in the new Italian scenario and the evaluation of energy management
actions.
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