dc.creator | Kouvelas V., Moschakis M. | en |
dc.date.accessioned | 2023-01-31T08:46:33Z | |
dc.date.available | 2023-01-31T08:46:33Z | |
dc.date.issued | 2022 | |
dc.identifier | 10.1109/SyNERGYMED55767.2022.9941467 | |
dc.identifier.isbn | 9781665461078 | |
dc.identifier.uri | http://hdl.handle.net/11615/75449 | |
dc.description.abstract | Load forecasting in the energy sector is an integral part of the electrical system as it is a criterion for its smooth and sustainable operation. The liberalization of electricity, the entry of RES into production and the digitization of supervisory means have brought more complexity to the system which translates into more variables. Machine learning models have the ability to process large numbers of parameters and this makes them attractive to researchers. In the context of this article, through the literature review, the prediction of electric load will be studied using artificial intelligence and specifically machine learning and deep learning which constitute the State of the Art in algorithms. © 2022 IEEE. | en |
dc.language.iso | en | en |
dc.source | SyNERGY MED 2022 - 2nd International Conference on Energy Transition in the Mediterranean Area, Proceedings | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142891090&doi=10.1109%2fSyNERGYMED55767.2022.9941467&partnerID=40&md5=b26ffd3dea4f2d1a90b27edee3bc1c50 | |
dc.subject | Deep learning | en |
dc.subject | Electric load forecasting | en |
dc.subject | Learning systems | en |
dc.subject | Neural networks | en |
dc.subject | Deep learning | en |
dc.subject | Electric load predictions | en |
dc.subject | Energy sector | en |
dc.subject | Integral part | en |
dc.subject | Learning techniques | en |
dc.subject | Load forecasting | en |
dc.subject | Machine-learning | en |
dc.subject | Neural-networks | en |
dc.subject | Short-term electric load forecasting | en |
dc.subject | Times series | en |
dc.subject | Electric power plant loads | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Short-term Electric Load Forecasting using Engineering and Deep Learning techniques | en |
dc.type | conferenceItem | en |