Implementation of innovative deep learning techniques in smart power systems
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Implementation of innovative deep learning techniques in smart power systems |
| 2. | Creator | Author's name, affiliation, country | Odugu Rama Devi; Lakireddy Balireddy College of Engineering; India |
| 2. | Creator | Author's name, affiliation, country | Pavan Kumar Kolluru; VFSTR deemed to be University; India |
| 2. | Creator | Author's name, affiliation, country | Nagul Shaik; GITAM (Deemed to be University); India |
| 2. | Creator | Author's name, affiliation, country | Kamparapu V. V. Satya Trinadh Naidu; Madanapalle Institute of Technology and Science; India |
| 2. | Creator | Author's name, affiliation, country | Chunduri Mohan; BVSR Engineering College; India |
| 2. | Creator | Author's name, affiliation, country | Pottasiri Chandra Mohana Rai; Koneru Lakshmaiah Education Foundation; India |
| 2. | Creator | Author's name, affiliation, country | Lakshmi Bhukya; Rajiv Gandhi University of Knowledge Technologies; India |
| 3. | Subject | Discipline(s) | Electrical and Computer |
| 3. | Subject | Keyword(s) | Deep learning techniques; Long short-term memory; Root mean square errors; Smart power systems; Support vector machine |
| 4. | Description | Abstract | The integration of deep learning techniques into smart power systems has gained significant attention due to their potential to optimize energy management, enhance grid reliability, and enable efficient utilization of renewable energy sources. This research article explores the enhanced application of deep learning techniques in smart power systems. It provides an overview of the key challenges faced by traditional power systems and presents various deep learning methodologies that can address these challenges. The results showed that the root mean square errors (RMSE) for the weekend power load forecast were 18.4 for the random forest and 18.2 for the long short-term memory (LSTM) algorithm, while 28.6 was predicted by the support vector machine (SVM) algorithm. The authors' approach provides the most accurate forecast (15.7). After being validated using real-world load data, this technique provides reliable power load predictions even when load oscillations are present. The article also discusses recent advancements, future research directions, and potential benefits of utilizing deep learning techniques in smart power systems. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2025-05-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/36868 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v38.i2.pp723-731 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 38, No 2: May 2025 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2025 LAKSHMI B This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. |