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CREATED:20250717T103039Z
LAST-MODIFIED:20250717T103039Z
DTSTAMP:20260906T021612Z
UID:1788650172@tuc.gr
SUMMARY:Παρουσίαση μεταπτυχιακής εργασιας κ.
  ΣΗΦΑΚΗ ΝΙΚΟΛΑΟΥ, Σχολή ΜΠΔ
LOCATION:
DESCRIPTION:https://www.pem.tuc.gr/el/chrisima/i
 merologio-ekdiloseon?tx_tucevents2_t
 uceventsdisplay%5Baction%5D=show&tx_
 tucevents2_tuceventsdisplay%5Bcontro
 ller%5D=Event&tx_tucevents2_tucevent
 sdisplay%5Bevent%5D=7957&cHash=41b1f
 77d3eb00fc3ffb49fe6fc01ef89\nΠΟΛΥΤΕΧ
 ΝΕΙΟ ΚΡΗΤΗΣ\n Σχολή Μηχανικών Παραγω
 γής και Διοίκησης\n Πρόγραμμα Μεταπτ
 υχιακών Σπουδών\n Διοίκηση Επιχειρήσ
 εων\n  \n ΠΑΡΟΥΣΙΑΣΗ ΜΕΤΑΠΤΥΧΙΑΚΗΣ Ε
 ΡΓΑΣΙΑΣ\n Τρίτη, 22 Ιουλίου 2025, 12
 :00\n https://tuc-gr.zoom.us/j/42672
 28881?pwd=SFNJdUFOR2ZTQjFKTlF3RkFKeF
 lwQT09\n Ονοματεπώνυμο: ΣΗΦΑΚΗΣ ΝΙΚΟ
 ΛΑΟΣ\n Θέμα: Πλαίσιο λήψης αποφάσεων
  και βελτιστοποίησης σχεδιασμού για 
 την διαστασιολόγηση υβριδικών συστημ
 άτων ανανεώσιμων πηγών ενέργειας σε 
 νησιά της Μεσογείου\n Title: A decis
 ion-making and planning optimization
  framework for implementing Hybrid R
 enewable Energy Systems in Mediterra
 nean isles\n Εξεταστική Επιτροπή\n \
 n \nΖΟΠΟΥΝΙΔΗΣ ΚΩΝΣΤΑΝΤΙΝΟΣ, Καθηγητ
 ής (επιβλέπων)\n \nΔΟΥΜΠΟΣ ΜΙΧΑΗΛ, Κ
 αθηγητής\n \nΚΟΝΤΟΓΙΑΝΝΗΣ ΘΩΜΑΣ, Καθ
 ηγητής\n \n Περίληψη\n This thesis p
 resents an integrated, AI-enhanced d
 ecision-making and planning optimiza
 tion framework for the deployment of
  Hybrid Renewable Energy Systems (HR
 ES) in Mediterranean insular context
 s. It addresses the multidimensional
  challenge of transitioning non-inte
 rconnected or partially interconnect
 ed island grids toward energy autono
 my, economic viability, and deep dec
 arbonization. The work introduces a 
 robust methodology that combines ene
 rgy demand profiling, stakeholder-in
 formed decision criteria, and AI-dri
 ven weight extraction using Random F
 orest regression, SHAP values, and t
 he CRITIC method. The framework simu
 lates and evaluates 14 distinct syst
 em configurations, integrating photo
 voltaic (PV), wind, tidal, and wave 
 resources with energy storage soluti
 ons, via a custom Genetic Algorithm 
 (GA) model. The GA incorporates a no
 vel adaptive mutation and restart me
 chanism to escape local optima, yiel
 ding convergence-optimized solutions
  tailored to real insular constraint
 s. Scenario ranking is performed usi
 ng a multi-criteria decision-making 
 approach based on the TOPSIS method,
  informed by AI-extracted criteria w
 eights. Comparative analysis of weig
 hting strategies reveals consistent 
 identification of the most suitable 
 configuration, validating the robust
 ness of the proposed approach. The m
 odel is designed to be modular, adap
 table to diverse island conditions, 
 and fully transparent in its evaluat
 ion of trade-offs across economic, e
 nvironmental, social, political, and
  technical dimensions. Validation is
  achieved through a case study on Cr
 ete, incorporating stakeholder feedb
 ack, dynamic load patterns, and rene
 wable potential data. The resulting 
 framework demonstrates its capacity 
 to inform sustainable energy plannin
 g with scientific rigor and operatio
 nal relevance. Lastly, this research
  contributes to filling a documented
  gap in HRES optimization literature
 , namely the absence of integrated, 
 data-driven, stakeholder-informed MC
 DM models tailored to the insular en
 ergy context. The proposed framework
  advances methodological practices f
 or sustainable energy transition pla
 nning and provides a replicable tool
  for decision-makers engaged in isla
 nd energy greenification and resilie
 nce strategies.\n
STATUS:CONFIRMED
ORGANIZER;RSVP=FALSE;CN=TUC;CUTYPE=TUC:mailto:webmaster@tuc.gr
DTSTART:20250722T120000
DTEND:20250722T130000
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