Simulation of wind-battery microgrid based on short-term wind power forecasting

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dc.contributor.authorGenikomsakis, Konstantinos N.
dc.contributor.authorLópez, Sergio
dc.contributor.authorDallas, Panagiotis I.
dc.contributor.authorIoakimidis, Christos S.
dc.date.accessioned2026-03-06T16:38:26Z
dc.date.available2026-03-06T16:38:26Z
dc.date.issued2017-11-06
dc.date.updated2026-03-06T16:38:26Z
dc.description.abstractThe inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN) clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.en
dc.description.sponsorshipThis work was funded by the EC under the FP7 RE-SIZED 621408 (Research Excellence for Solutions and Implementation of Net-Zero Energy City Districts) projecten
dc.identifier.citationGenikomsakis, K. N., Lopez, S., Dallas, P. I., & Ioakimidis, C. S. (2017). Simulation of wind-battery microgrid based on short-term wind power forecasting. Applied Sciences (Switzerland), 7(11). https://doi.org/10.3390/APP7111142
dc.identifier.doi10.3390/APP7111142
dc.identifier.eissn2076-3417
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5360
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2017 by the authors
dc.subject.otherArtificial neural network
dc.subject.otherEnergy management
dc.subject.otherMicrogrid
dc.subject.otherMonte Carlo simulation
dc.subject.otherWind power forecasting
dc.titleSimulation of wind-battery microgrid based on short-term wind power forecastingen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue11
oaire.citation.titleApplied Sciences (Switzerland)
oaire.citation.volume7
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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