An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques
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...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10606478/ |
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| author | Md. Abul Kalam Abhinav Saxena Md. Zahid Hassnain Amit Kumar Dash Jay Singh Gyanendra Kumar Singh |
| author_facet | Md. Abul Kalam Abhinav Saxena Md. Zahid Hassnain Amit Kumar Dash Jay Singh Gyanendra Kumar Singh |
| author_sort | Md. Abul Kalam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4c3be48f1735457eb9ba411f4dcd5ec8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4c3be48f1735457eb9ba411f4dcd5ec82025-08-20T02:48:46ZengIEEEIEEE Access2169-35362024-01-011217892817894010.1109/ACCESS.2024.343280510606478An Explicit Investigation in Demand Side Management Based on Artificial Intelligence TechniquesMd. Abul Kalam0https://orcid.org/0000-0001-5459-7605Abhinav Saxena1https://orcid.org/0000-0003-2365-4333Md. Zahid Hassnain2Amit Kumar Dash3https://orcid.org/0000-0002-9384-2121Jay Singh4https://orcid.org/0000-0002-2298-9214Gyanendra Kumar Singh5https://orcid.org/0000-0003-3765-9071Department of Electrical Engineering, BIT Sindri, Dhanbad, Jharkhand, IndiaDepartment of Electrical Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, IndiaDepartment of Electrical Engineering, BIT Sindri, Dhanbad, Jharkhand, IndiaNoida Institute of Engineering and Technology, Greater Noida, IndiaDepartment of Electrical and Electronics Engineering, GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, IndiaSchool of Mechanical, Chemical and Materials Engineering, Adama Science and Technology University, Adama, EthiopiaThe 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.https://ieeexplore.ieee.org/document/10606478/Demand side managementmulti-agent systemartificial neural networkmachine learningnatural inspired algorithm |
| spellingShingle | Md. Abul Kalam Abhinav Saxena Md. Zahid Hassnain Amit Kumar Dash Jay Singh Gyanendra Kumar Singh An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques IEEE Access Demand side management multi-agent system artificial neural network machine learning natural inspired algorithm |
| title | An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques |
| title_full | An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques |
| title_fullStr | An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques |
| title_full_unstemmed | An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques |
| title_short | An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques |
| title_sort | explicit investigation in demand side management based on artificial intelligence techniques |
| topic | Demand side management multi-agent system artificial neural network machine learning natural inspired algorithm |
| url | https://ieeexplore.ieee.org/document/10606478/ |
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