Energy efficiency in a smart home with an intelligent neurofuzzy paradigm

Date

2012

Authors

Shahgoshtasbi, Dariush

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Abstract

By using Demand Side Management (DSM) we can shift electrical load from peak demand time to other periods. An automated Intelligent Energy Management System (iEMS) was developed in this research where building energy consumption is modified in a dynamic setting. The system is able to not only manage renewable energy resources but also to consider users' preferences and behaviors. In addition, it finds optimal energy scheduling according to the dynamic notion of price. A new topology of neural network is introduced and acts as an associative memory with a crystal type structure, which can be modified easily. Two models for an automated iEMS in residential level are presented. The proposed systems have two subsystems: fuzzy subsystem and an intelligent lookup table. The best energy-efficiency scenarios in different situations can be automatically found. A combined enhanced model was developed. The actual potential of intelligent automated decisions in a residential home is demonstrated by coupling iEMS with Gridlab-D (a U.S. Department of Energy funded software). The iEMS program is able to regulate the energy consumption of appliances based on the different situations and rule scenarios. Typical home appliances (such as water heater, air conditioner, light, solar panel, battery storage, refrigerator, freezer, dishwasher, washer and dryer) are simulated in GridLab-D and their energy consumption, cost and representative savings are illustrated when the iEMS is implemented. Because of different weather conditions and configurations of residential homes across the United States, four (4) cities were chosen to represent the U.S Climate in the north, south, east and west (Madison, San Antonio, New York City and San Diego, respectively). This was also done at different times of the year (January and August) for one week to consider seasonal changes. Our findings show that savings in the order of 15-30% can be achieved when an intelligent Energy Management System ( iEMS) is used with controllable appliances.

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Keywords

Demand Response, Energy Efficiency, Fuzzy Logic, Neural Networks, Smart Grid, Smart Home

Citation

Department

Electrical and Computer Engineering