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Napovedovanje proizvodnje električne energije sončnih elektrarn povezanih v mrežo
ID Murko, Anže (Author), ID Podržaj, Primož (Mentor) More about this mentor... This link opens in a new window

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Abstract
Proizvodnja električne energije sončnih elektrarn je zaznamovana z veliko spremenljivostjo, kar predstavlja izziv pri upravljanju energetskih omrežij. Z namenom izboljšanja napovednih rezultatov proizvodnje energije sončnih elektrarn je bil razvit nov pristop. Slednji pri napovedovanju proizvodnje centralne elektrarne uporablja različno število vključenih sosednjih elektran v napoved. Odvisnost napovednih rezultatov modelov je bolj odvisna od števila vključenih sosednjih elektrarn kot pa od same topologije mreže. Izvedli smo optimizacijo hiperparametrov modelov in napovedne rezultate primerjali z obstoječimi raziskavami. Ugotovili smo, da neuporaba meteoroloških podatkov rezultira v slabših napovednih rezultatih.

Language:Slovenian
Keywords:električna energija, sončne elektrarne, napovedovanje, strojno učenje, električno omrežje
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Place of publishing:Ljubljana
Publisher:[A. Murko]
Year:2023
Number of pages:XXII, 61 str.
PID:20.500.12556/RUL-148757 This link opens in a new window
UDC:502.21:523.9:620.9(043.2)
COBISS.SI-ID:167625219 This link opens in a new window
Publication date in RUL:31.08.2023
Views:1153
Downloads:101
Metadata:XML DC-XML DC-RDF
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MURKO, Anže, 2023, Napovedovanje proizvodnje električne energije sončnih elektrarn povezanih v mrežo [online]. Master’s thesis. Ljubljana : A. Murko. [Accessed 15 July 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=148757
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Secondary language

Language:English
Title:Forecasting the electricity generation of grid-connected solar power plants
Abstract:
Electricity production from solar power plants is characterised by volatility, which presents a challenge in the management of electrical grids. A new approach was developed in order to improve the predictive results of the energy production of solar power plants. The latter uses a different number of included neighbouring power plants in the energy forecast of the central power plant. The dependence of the predictive results depends more on the number of neighbouring power plants included, than on the topology of the network. We performed the optimisation of the model's hyperparameters and compared the predictive results with the existing research. We found out, that not using meteorological data results in worse forecasting results.

Keywords:electricity, solar power plants, forecasting, machine learning, electrical grid

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