Short-term Load Forecasting in Grid-connected Microgrid

dc.contributor.authorIzzatillaev, J.
dc.contributor.authorYusupov, Z.
dc.date.accessioned2024-09-29T16:20:44Z
dc.date.available2024-09-29T16:20:44Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 -- 25 April 2019 through 26 April 2019 -- Istanbul -- 150223en_US
dc.description.abstractThe integration of distributed energy resources (DER) into main grid need to consider the influence of many factors. One of them is the determination of electric load in Microgrid. Creating a feasible and efficient Microgrid based on the predicted power load is more relevant. The paper analyzes the forecasting of the electric energy consumption in Microgrids, analyzes the area of applicability, advantages and disadvantages of short-term forecasting methods of power consumption. Two methods-Group Method of Data Handling (GMDH) and Artificial Neural Networks (ANN) are used to determine short-term load forecasting. © 2019 IEEE.en_US
dc.identifier.doi10.1109/SGCF.2019.8782424
dc.identifier.endpage75en_US
dc.identifier.isbn978-172811315-9
dc.identifier.scopus2-s2.0-85070959737en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage71en_US
dc.identifier.urihttps://doi.org/10.1109/SGCF.2019.8782424
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9285
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectforecastingen_US
dc.subjectloaden_US
dc.subjectmicrogriden_US
dc.titleShort-term Load Forecasting in Grid-connected Microgriden_US
dc.typeConference Objecten_US

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