Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach.
- Title
- Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach.
- Creator
- Bani Ahmad A.Y.A.; William P.; Uike D.; Murgai A.; Bajaj K.K.; Deepak A.; Shrivastava A.
- Description
- Maintaining a consistent supply of power is essential for the well-being of the economy, the public, and one's own health. The generation of energy, as well as its distribution, monitoring, and management, are all undergoing fundamental changes as a result of the implementation of a smart grid (SG), which is authorised to include communication technology and sensors into power systems. There are a lot of problems that need to be fixed before the interoperability of the smart grid can be determined. The integration of renewable energy sources and smart grid technology market size and energy management is a sustainable solution to the problem of energy demand management. The importance work quickly toward the development of an efficient Energy Management Model (EMM) that integrates smart grids and renewable energy sources. When it comes to the modelling of complex and non-linear data, machine learning (ML), Internet of Things (IoT) approaches often perform better than statistical models. So, utilizing a machine learning approach for the EMM is a good option since it simplifies the EMM by generating a single trained model to anticipate its performance characteristics across all conditions. This may be accomplished via the use of an EMM created using an ML method. It was recommended that a certain flexibility sample be used as a control mechanism for incursion into the smart grid. The outcomes of the experiment indicate that the demand-side management (DSM) device is more resistant to infiltration and is enough to lower the energy usage of the smart grid. 2024, Ismail Saritas. All rights reserved.
- Source
- International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 2s, pp. 581-590.
- Date
- 2024-01-01
- Publisher
- Ismail Saritas
- Subject
- Energy Management Model (EMM); Internet of Things (IOT); Machine Learning (ML); Smart Grid; Smart Grid
- Coverage
- Bani Ahmad A.Y.A., Department of Accounting and Finance Science, Faculty of Business, Middle East University, 8788, Amman, 11831, Jordan; William P., Research and Development, Department of Information Technology, Sanjivani College of Engineering, Savitribai Phule Pune University, Pune, India; Uike D., Department of MBA, Dr. Ambedkar Institute of Management Studies and Research, Maharashtra, Nagpur, India; Murgai A., School of Business and Management, Christ University, Lavasa, Pune, India; Bajaj K.K., RNB, Global University, Rajasthan, Bikaner, India; Deepak A., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Tamilnadu, Chennai, India; Shrivastava A., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamilnadu, Chennai, India
- Rights
- Restricted Access
- Relation
- ISSN: 21476799
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Bani Ahmad A.Y.A.; William P.; Uike D.; Murgai A.; Bajaj K.K.; Deepak A.; Shrivastava A., “Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach.,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/13811.