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DC poleHodnotaJazyk
dc.contributor.authorFaraji, Jamal
dc.contributor.authorKetabi, Abbas
dc.contributor.authorHashemi Dezaki, Hamed
dc.contributor.authorShafie-Khah, Miadreza
dc.contributor.authorKatalao, Joao P.S.
dc.date.accessioned2020-09-21T10:00:14Z-
dc.date.available2020-09-21T10:00:14Z-
dc.date.issued2020
dc.identifier.citationFARAJI, J., KETABI, A., HASHEMI DEZAKI, H., SHAFIE-KHAH, M., KATALAO, J. P. Optimal day-ahead scheduling and operation of the prosumer by considering corrective actions based on very short-term load forecasting. IEEE Access, 2020, roč. 8, č. 2020, s. 83561-83582. ISSN 2169-3536.en
dc.identifier.issn2169-3536
dc.identifier.uri2-s2.0-85084949700
dc.identifier.urihttp://hdl.handle.net/11025/39670
dc.format22 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE Accessen
dc.rights© IEEEen
dc.titleOptimal day-ahead scheduling and operation of the prosumer by considering corrective actions based on very short-term load forecastingen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedEnergy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise shortterm LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.en
dc.subject.translatedload forecasting (LF)en
dc.subject.translatedmulti-layer perceptron artificial neural network (ANN-MLP)en
dc.subject.translatedoptimal operation and schedulingen
dc.subject.translatedprosumeren
dc.subject.translatedbattery energy storage system (BESS)en
dc.subject.translatedrenewable energy sources (RESs)en
dc.identifier.doi10.1109/ACCESS.2020.2991482
dc.type.statusPeer-revieweden
dc.identifier.document-number549502200152
dc.identifier.obd43930121
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