A new method for generating short-term power forecasting based on artificial neural networks and optimization methods for solar photovoltaic power plants

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dc.contributor.authorDemirdelen, Tugce
dc.contributor.authorOzge Aksu, Inayet
dc.contributor.authorEsenboga, Burak
dc.contributor.authorAygul, Kemal
dc.contributor.authorEkinci, Firat
dc.contributor.authorBilgili, Mehmet
dc.date.accessioned2025-01-06T17:29:58Z
dc.date.available2025-01-06T17:29:58Z
dc.date.issued2019
dc.description.abstractIn recent times, solar PV power plants have been used worldwide due to their high solar energy potential. Although the PV power plants are highly preferred, the main disadvantage of the system is that the output power characteristics of the system are unstable. As PV power plant system is connected to the grid side, unbalanced power flow effects all systems controls. In addition, the load capacitys is not exactly known. For this reason, it has become an important issue to be known correctly in PV output power and their time-dependent changes. The main aim of this work is to eliminate power plant instability due to the output power imbalance. For the short-term, power prediction is estimated by real-time data of 1 MW PV power plant in use. Estimation power data are compared with real-time data and precision of the proposed method is demonstrated. In the first phase, traditional artificial intelligence algorithms are used. Then, these algorithms are trained with swarm based optimization methods and the performance analyses are presented in detail. Among all the algorithms used, the algorithm with the lowest error is determined. Thus, this study provides useful information and techniques to help researchers who are interested in planning and modeling PV power plants. © Springer Nature Singapore Pte Ltd. 2019.
dc.identifier.doi10.1007/978-981-13-6151-7_8
dc.identifier.endpage189
dc.identifier.issn1612-1287
dc.identifier.scopus2-s2.0-85062276381
dc.identifier.scopusqualityQ4
dc.identifier.startpage165
dc.identifier.urihttps://doi.org/10.1007/978-981-13-6151-7_8
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1433
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Verlag
dc.relation.ispartofPower Systems
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFirefly algorithm
dc.subjectNeural networks
dc.subjectOptimization methods
dc.subjectPhotovoltaic plants
dc.subjectPower prediction
dc.subjectShort-term
dc.titleA new method for generating short-term power forecasting based on artificial neural networks and optimization methods for solar photovoltaic power plants
dc.typeBook Chapter

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