Modified Intelligent Energy Management system in a smart house

Demand response has an important role in improving energy efficiency. By using it, we are able to 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, which is the modified version of our last work, an intelligent EMS in a smart house is presented
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. It has two components, fuzzy component and intelligent lookup table. Fuzzy component is in the EMS and makes the proper output for intelligent lookup table based on i ts fuzzy rules and inputs. The second component which i ts core is an associative neural network is moved to smart appliances. So each appliance has a separate intelligent lookup table. They are able to map inputs to desired outputs. They take three types of inputs which come from fuzzy component, outside sensors and other smart appliances. So, all the appliances should send their control bits to each other. Whatever is trained in these lookup table s are different scenarios in different conditions. This system is able to find the best energy efficiency scenario in different situations.