Energy efficiency in a smart house with an intelligent neuro-fuzzy lookup table

Demand response has an important role in improving energy efficiency. By using it, we can shift electrical load from peak demand time to other periods which is usually in response to price signal. In residential level and in a dynamic pricing system which modification of energy consumption is unrecognized by a consumer, using an automated Energy Management System (EMS) should be considered. In this paper, a new intelligent EMS in a smart house is presented. It has two parts, fuzzy part and intelligent lookup table. Fuzzy part based on its fuzzy rules and inputs makes the proper output for intelligent lookup table. The second part which its core is an associative neural network is able to map inputs to desired outputs. It takes two types of inputs which come from fuzzy part and outside sensors. 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.