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dc.contributor.authorHamza Zafar, Muhammad
dc.contributor.authorMujeeb Khan, Noman
dc.contributor.authorMansoor, Majad
dc.contributor.authorFeroz Mirza, Adeel
dc.contributor.authorKumayl Raza Moosavi, Syed
dc.contributor.authorSanfilippo, Filippo
dc.identifier.citationHaZafar, M., Mujeeb Khan, N., Mansoor, M., Feroz Mirza, A., Kumayl Raza Moosavi, S. & Sanfilippo, F. (2022). Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems. Energy Conversion and Management, 258, Artikkel 115564.en_US
dc.description.abstractLarge scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleAdaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systemsen_US
dc.title.alternativeAdaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systemsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2022 The Author(s)en_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.subject.nsiVDP::Teknologi: 500::Maskinfag: 570::Maskinteknisk energi- og miljøteknologi: 573en_US
dc.source.journalEnergy Conversion and Managementen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal