Bibliographic Details
| Title: |
An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques |
| Authors: |
Md. Abul Kalam, Abhinav Saxena, Md. Zahid Hassnain, Amit Kumar Dash, Jay Singh, Gyanendra Kumar Singh |
| Source: |
IEEE Access, Vol 12, Pp 178928-178940 (2024) |
| Publisher Information: |
IEEE, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Demand side management, multi-agent system, artificial neural network, machine learning, natural inspired algorithm, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
The several artificially intelligent techniques used in demand-side management (DSM) are exhaustively reviewed in this article. The objective of the demand-side management is to connect and disconnect the available generating units with the variable loads with the objective of meeting peak load and base load demand. The meeting of load demand with the adjustment in available generating units is accompanied by demand-side management. It is observed that ANN is utilized for short-term load and pricing forecasting, and other nature-encouraged optimization techniques like swarm intelligence, game theory, deep learning methods, etc. may be used as speculation methods because these optimization techniques are less precise. Demand-side management involves highly complicated losses in all existing methodologies that have been controlled and reduced by artificial intelligence and machine learning. Smart pricing for customers results from increasing the economic efficiency of consumption by promoting energy load demand during off-peak hours and discouraging energy load demand during peak hours. Less fuel consumption also helps to reduce carbon emissions from these power generation projects, which helps power suppliers save on additional fuel costs due to severe and unpredictable margin variations in power generation. The various aspects of the charging of electric vehicles using demand-side management, considering the clustering methods and forecasting strategies with brief descriptions, data, range have been assessed by using the different artificial intelligence and machine learning methods. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10606478/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3432805 |
| Access URL: |
https://doaj.org/article/4c3be48f1735457eb9ba411f4dcd5ec8 |
| Accession Number: |
edsdoj.4c3be48f1735457eb9ba411f4dcd5ec8 |
| Database: |
Directory of Open Access Journals |
