State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
| dc.authorid | yaghoubi, elnaz/0000-0002-8672-4178 | |
| dc.authorid | Wazirali, Raniyah/0000-0002-3609-9351 | |
| dc.authorid | Alshwaiyat, Rami/0000-0003-3913-6397 | |
| dc.authorid | Vakili, Amir Hossein/0000-0001-8920-172X | |
| dc.contributor.author | Wazirali, Raniyah | |
| dc.contributor.author | Yaghoubi, Elnaz | |
| dc.contributor.author | Abujazar, Mohammed Shadi S. | |
| dc.contributor.author | Ahmad, Rami | |
| dc.contributor.author | Vakili, Amir Hossein | |
| dc.date.accessioned | 2024-09-29T15:55:24Z | |
| dc.date.available | 2024-09-29T15:55:24Z | |
| dc.date.issued | 2023 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description.abstract | Forecasting renewable energy efficiency significantly impacts system management and operation because more precise forecasts mean reduced risk and improved stability and reliability of the network. There are several methods for forecasting and estimating energy production and demand. This paper discusses the significance of artificial neural network (ANN), machine learning (ML), and Deep Learning (DL) techniques in predicting renewable energy and load demand in various time horizons, including ultra-short-term, short-term, mediumterm, and long-term. The purpose of this study is to comprehensively review the methodologies and applications that utilize the latest developments in ANN, ML, and DL for the purpose of forecasting in microgrids, with the aim of providing a systematic analysis. For this purpose, a comprehensive database from the Web of Science was selected to gather relevant research studies on the topic. This paper provides a comparison and evaluation of all three techniques for forecasting in microgrids using tables. The techniques mentioned here assist electrical engineers in becoming aware of the drawbacks and advantages of ANN, ML, and DL in both load demand and renewable energy forecasting in microgrids, enabling them to choose the best techniques for establishing a sustainable and resilient microgrid ecosystem. | en_US |
| dc.identifier.doi | 10.1016/j.epsr.2023.109792 | |
| dc.identifier.issn | 0378-7796 | |
| dc.identifier.issn | 1873-2046 | |
| dc.identifier.scopus | 2-s2.0-85170824427 | en_US |
| dc.identifier.scopusquality | Q1 | en_US |
| dc.identifier.uri | https://doi.org/10.1016/j.epsr.2023.109792 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/4616 | |
| dc.identifier.volume | 225 | en_US |
| dc.identifier.wos | WOS:001075080000001 | en_US |
| dc.identifier.wosquality | Q2 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Science Sa | en_US |
| dc.relation.ispartof | Electric Power Systems Research | en_US |
| dc.relation.publicationcategory | Diğer | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Renewable energy forecasting | en_US |
| dc.title | State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques | en_US |
| dc.type | Review | en_US |
