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Industry 4.0 Based Efficient Energy Management in Microgrid

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Field Value
 
Title Industry 4.0 Based Efficient Energy Management in Microgrid
 
Creator Neeraj
Gupta, Pankaj
Tomar, Anuradha
 
Subject BPN
Distributed power resources
Energy management
Machine learning
 
Description 287-296
Industry 4.0 which includes new technologies such as artificial intelligence, machine learning, and the internet of things etc. has
brought the revolution in the field of energy management of a microgrid. Energy management is the backbone of a microgrid that
needs to be controlled efficiently for a low system failure. There are a lot of issues, such as the intermittent nature of generation,
proper voltage distribution, and harmonics, which may arise while implementing an energy management for a microgrid. Machine
learning establishes the core of industry 4.0 and is one of the best-suited methods to mitigate such challenges in the current industry
4.0 scenario. In this paper, a Back Propagation Neural Network (BPNN) based machine learning approach is applied for forecasting
of a photovoltaic (PV) generation in a microgrid to deal with its intermittent nature for efficient energy management. Further, a
firefly optimization technique is utilized to mitigate the harmonics in the voltage. This model is implemented on a real dataset of a
solar power plant in Delhi, India. The proposed approach achieves the results of high precision, recall, and accuracy, which shows
the efficiency of the system to monitor and regulate uncertainties in the PV microgrid systems.
 
Date 2023-02-08T04:56:03Z
2023-02-08T04:56:03Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61354
https://doi.org/10.56042/jsir.v82i2.70255
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]