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dc.contributor.authorSanjari, Mohammad Javad
dc.contributor.authorGooi, HB
dc.date.accessioned2021-11-03T06:24:31Z
dc.date.available2021-11-03T06:24:31Z
dc.date.issued2017
dc.identifier.issn0885-8950
dc.identifier.doi10.1109/TPWRS.2016.2616902
dc.identifier.urihttp://hdl.handle.net/10072/409742
dc.description.abstractThis paper presents a method to forecast the probability distribution function (PDF) of the generated power of PV systems based on the higher order Markov chain (HMC). Since the output power of the PV system is highly influenced by ambient temperature and solar irradiance, they are used as important features to classify different operating conditions of the PV system. The classification procedure is carried out by applying the pattern discovery method on the historical data of the mentioned variables. An HMC is developed based on the categorized historical data of PV power in each operating point. The 15-min ahead PDF of the PV output power is forecasted through the Gaussian mixture method (GMM) by combining several distribution functions and by using the coefficients defined based on parameters of the HMC-based model. In order to verify the proposed method, the genetic algorithm is applied to minimize a well-defined objective function to achieve the optimal GMM coefficients. Numerical tests using real data demonstrate that the forecast results follow the real probability distribution of the PV power well under different weather conditions.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofpagefrom2942
dc.relation.ispartofpageto2952
dc.relation.ispartofissue4
dc.relation.ispartofjournalIEEE Transactions on Power Systems
dc.relation.ispartofvolume32
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchElectronics, sensors and digital hardware
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode4009
dc.subject.keywordsScience & Technology
dc.subject.keywordsTechnology
dc.subject.keywordsEngineering, Electrical & Electronic
dc.subject.keywordsEngineering
dc.subject.keywordsHigher order Markov chain
dc.titleProbabilistic forecast of PV power generation based on higher order Markov chain
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSanjari, MJ; Gooi, HB, Probabilistic forecast of PV power generation based on higher order Markov chain, IEEE Transactions on Power Systems, 2017, 32 (4), pp. 2942-2952
dc.date.updated2021-11-03T06:23:34Z
gro.hasfulltextNo Full Text
gro.griffith.authorSanjari, Mohammad


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