Smart Grids: AI-Enabled Energy Management and Demand Forecasting
The application of the Smart Grids with Artificial intelligence - AI has transformed the sector of management and demand forecasting dramatically. The objective of such research is to develop AI based prospective energy management system which involves advanced tool as LSTM for demand forecasting. The figure of merit stressed over the research in the literature has been data preprocessing, feature selection and classification techniques performed for improving the model performance. In order to provide an evidence of the success of this method in analyzing real time energy data, this is a case study of the use of Scikit Learn library. So, removing missing value, normalising the data, feature selection with RFE, classification with GBM. With the help of AI technology, the results suggest better accuracy of prediction and scalability, and is thus deemed a future of smart grid management.
Authors
- Harshal Patil (ORCID: https://orcid.org/0000-0002-9333-2295)
- Sreeanandan Sreeanandan
- Tanusha Mittal
- Najmuddin Aamer (ORCID: https://orcid.org/0000-0001-6621-3584)
- JehanKadhim Shareef Al-Safi
Institutions
- Thi Qar University (IQ)
- IILM Institute for Higher Education (IN)
- PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH (IN)
- Graphic Era University (IN)
- Saveetha University (IN)
Publication Details
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1109/icfts62006.2025.11031861
- Citations
- 2
- Primary Topic
- Energy Load and Power Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 1.46