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Industry 4.0 Based Efficient Energy Management in Microgrid


Affiliations
1 Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi 110 006, India
2 Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Delhi 110 078, India
 

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.

Keywords

BPN, Distributed Power Resources, Energy Management, Machine Learning.
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  • Industry 4.0 Based Efficient Energy Management in Microgrid

Abstract Views: 50  |  PDF Views: 42

Authors

Neeraj
Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi 110 006, India
Pankaj Gupta
Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi 110 006, India
Anuradha Tomar
Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Delhi 110 078, India

Abstract


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.

Keywords


BPN, Distributed Power Resources, Energy Management, Machine Learning.

References