Industry 4.0 Based Efficient Energy Management in Microgrid

4Citations
Citations of this article
48Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Neeraj, Gupta, P., & Tomar, A. (2023). Industry 4.0 Based Efficient Energy Management in Microgrid. Journal of Scientific and Industrial Research, 82(2), 287–296. https://doi.org/10.56042/jsir.v82i2.70255

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free