A New Intelligent Neuro-Fuzzy Paradigm for Energy-Efficient Homes

1  Abstract— Demand response, which is the action voluntarily taken by a consumer to adjust amount or timing of its energy consumption, has an important role in improving energy efficiency. With demand response, we can shift electrical load from peak demand time to other periods based on changes in price signal. At residential level, automated Energy Management System (EMS) have been developed to assist users in responding to price changes in dynamic pricing systems. In this paper, a new intelligent EMS (iEMS) in a smart house is presented. It consists of two parts: fuzzy subsystem and intelligent lookup table. Fuzzy subsystem is based on its fuzzy rules and inputs which produces the proper output for intelligent lookup table. The second part, whose core is a new model of an associative neural network, is able to map inputs to desired outputs. The structure of the associative neural network is presented and discussed. The intelligent lookup table takes three types of inputs which come from fuzzy subsystem, outside sensors and feedback outputs. Whatever is trained in this lookup table are different scenarios in different conditions. This system is able to find the best energy efficiency scenario in different situations.