1. Energy management system for residential buildings based on fuzzy logic: design and implementation in smart‐meter
- Author
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Adrian Chojecki, Michał Rodak, Arkadiusz Ambroziak, and Piotr Borkowski
- Subjects
energy management algorithm ,building management system ,Computer Networks and Communications ,Smart meter ,Energy management ,Computer science ,building management systems ,energy management system ,smart metre ,ems ,rule base ,distributed power generation ,residential buildings ,energy consumption ,energy management systems ,Electrical and Electronic Engineering ,renewable energy sources ,energy efficiency ,energy conservation ,Building management system ,power engineering computing ,business.industry ,maximum added value ,smart meters ,object-oriented programming ,Energy consumption ,Reliability engineering ,Energy conservation ,smart power grids ,Smart grid ,Distributed generation ,modern buildings ,fuzzy logic ,smart grid technologies ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,renewable energy source ,smart metering systems ,business ,lcsh:TK1-9971 ,Information Systems ,Efficient energy use - Abstract
Advances in distributed generation and increased contribution of renewable energy source (RES) require development of smart grid technologies. Smart metering systems, as a part of smart grid technologies, in cooperation with modern buildings equipped with building management system allows for improvement of energy efficiency. It is possible to partially cover the power demand of a building from the local RESs. However, in order to ensure maximum added value, energy management system (EMS) is essential. This article presents the project and practical implementation of an EMS implemented in smart-meter. The designed system is based on an original algorithm using fuzzy logic. The rule base was created in FCL language and the implementation was carried out in C++ with the object-oriented programming (OOP). For the efficiency rating indicator, peak-to-average ratio (PAR) was selected. This ratio depending on the daily load profile decreased within a range from 15 to 54%, and the average value was 30%. The proposed energy management algorithm helps to reduce energy consumption at peak demand by 34%, with the total reduction of energy consumption during the day of 7%. The described solution demonstrates a potential for real implementation and was tested in hardware.
- Published
- 2020
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