Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/18528
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aslam, Sheraz | - |
dc.contributor.author | Herodotou, Herodotos | - |
dc.contributor.author | Ayub, Nasir | - |
dc.contributor.author | Mohsin, Syed Muhammad | - |
dc.date.accessioned | 2020-07-21T08:00:46Z | - |
dc.date.available | 2020-07-21T08:00:46Z | - |
dc.date.issued | 2020-02-13 | - |
dc.identifier.citation | 17th International Conference on Frontiers of Information Technology, Islamabad, Pakistan,16-18 December 2019 | en_US |
dc.identifier.isbn | 978-1-7281-6625-4 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/18528 | - |
dc.description.abstract | In the last few years, carbon emissions and energy demand have increased dramatically around the globe due to a surge in population and energy-consuming devices. The integration of renewable energy resources (RERs) in a power supply system provides an efficient solution in terms of low energy cost with lower carbon emissions. However, renewable sources like solar panels have irregular nature of power generation because of their dependence on weather conditions, such as solar radiation, humidity, and temperature. Therefore, to tackle this intermittent nature of solar energy, power prediction is necessary for efficient energy management. Deep learning and machine learning-based methods have frequently been implemented for energy forecasting in the literature. The current work summarizes the state-of-theart deep learning-based methods that are proposed to forecast the solar power for proper energy management. We also explain the methodologies of solar energy forecasting along with their outcomes. At the end, future challenges and opportunities are uncovered in the application of deep and machine learning in this area. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © IEEE | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Artificial neural network | en_US |
dc.subject | Deep learning based techniques | en_US |
dc.subject | Energy forecasting | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Microgrid | en_US |
dc.subject | Weather forecasting | en_US |
dc.title | Deep learning based techniques to enhance the performance of microgrids: A review | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | COMSATS University Islamabad | en_US |
dc.collaboration | Federal Urdu University of Arts | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.country | Cyprus | en_US |
dc.country | Pakistan | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.relation.conference | International Conference on Frontiers of Information Technology | en_US |
dc.identifier.doi | 10.1109/FIT47737.2019.00031 | en_US |
dc.identifier.scopus | 2-s2.0-85080132504 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85080132504 | - |
cut.common.academicyear | 2019-2020 | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.openairetype | conferenceObject | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0003-4305-0908 | - |
crisitem.author.orcid | 0000-0002-8717-1691 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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This item is licensed under a Creative Commons License