Ali, Amira Noor Farhanie and Sulaima, Mohamad Fani and Razak, Intan Azmira Wan Abdul and Kadir, Aida Fazliana Abdul and Mokhlis, Hazlie (2023) Artificial intelligence application in demand response: Advantages, issues, status, and challenges. IEEE ACCESS, 11. pp. 16907-16922. DOI https://doi.org/10.1109/ACCESS.2023.3237737.
Full text not available from this repository.Abstract
In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demand-side management solutions is also illustrated congruently.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education (MOHE) of Malaysia through the Fundamental Research Grant Scheme(FRGS) (FRGS/1/2021/FKE/F00465) |
| Uncontrolled Keywords: | Demand response; Optimization; Machine learning; Job shop scheduling; Task analysis; Support vector machines; Pricing; Artificial intelligence (AI); demand response (DR); demand side management (DSM); optimization algorithms |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 22 Nov 2023 06:08 |
| Last Modified: | 22 Nov 2023 06:08 |
| URI: | http://eprints.um.edu.my/id/eprint/38933 |
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