1. Energy flexibility in aluminium smelting : a long-term feasibility study based on the prospects of electricity load and photovoltaic productionMarko Bizjak, Niko Uremović, Domen Mongus, Primož Sukič, Gorazd Štumberger, Haris Salihagić Hrenko, Dragan Mikša, Stanislav Kores, Niko Lukač, 2024, original scientific article Keywords: energy flexibility, aluminium smelting, renewable energy, virtual battery, solar production Published in DKUM: 17.12.2024; Views: 0; Downloads: 5
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2. Novel GPU-accelerated high-resolution solar potential estimation in urban areas by using a modified diffuse irradiance modelNiko Lukač, Domen Mongus, Borut Žalik, Gorazd Štumberger, Marko Bizjak, 2024, original scientific article Abstract: In the past years various methods have been developed to estimate high-resolution solar potential in urban areas, by simulating solar irradiance over surface models that originate from remote sensing data. In general, this requires discretisation of solar irradiance models that estimate direct, reflective, and diffuse irradiances. The latter is most accurately estimated by an anisotropic model, where the hemispherical sky dome from arbitrary surface’s viewpoint consists of the horizon, the circumsolar and sky regions. Such model can be modified to incorporate the effects of shadowing from obstruction with a view factor for each sky region. However, state-of-the-art using such models for estimating solar potential in urban areas, only considers the sky view factor, and not circumsolar view factor, due to high computational load. In this paper, a novel parallelisation of solar potential estimation is proposed by using General Purpose computing on Graphics Processing Units (GPGPU). Modified anisotropic Perez model is used by considering diffuse shadowing with all three sky view factors. Moreover, we provide validation based on sensitivity analysis of the method’s accuracy with independent meteorological measurements, by changing circumsolar sky region’s half-angle and resolution of the hemispherical sky dome. Finally, the presented method using GPPGU was compared to multithreaded Central Processing Unit (CPU) approach, where on average a 70x computational speedup was achieved. Finally, the proposed method was applied over a urban area, obtained from Light Detection And Ranging (LiDAR) data, where the computation of solar potential was performed in a reasonable time. Keywords: solar energy, solar potential, anisotropic diffuse irradiance, LiDAR, GPGPU Published in DKUM: 17.12.2024; Views: 0; Downloads: 3
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6. Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite ImageryDomen Kavran, Domen Mongus, Borut Žalik, Niko Lukač, 2023, original scientific article Keywords: multispectral, Sentinel-2, superpixel, node, EfficientNetV2, GraphSAGE Published in DKUM: 23.05.2024; Views: 158; Downloads: 16
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7. Detection and Monitoring of Woody Vegetation Landscape Features Using Periodic Aerial PhotographyDamjan Strnad, Štefan Horvat, Domen Mongus, Danijel Ivajnšič, Štefan Kohek, 2023, original scientific article Keywords: woody vegetation landscape features, change detection, segmentation neural network, cyclic aerial photography, digital orthophoto Published in DKUM: 23.05.2024; Views: 196; Downloads: 16
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8. Učinkovit iterativni algoritem učenja razložljivih značilnic za izboljšano klasifikacijo : doktorska disertacijaDino Vlahek, 2024, doctoral dissertation Abstract: V doktorski disertaciji opišemo nov postopek učenja razložljivih značilnic za klasifikacijske namene. Značilnice med vsako iteracijo rekombiniramo na osnovi vnaprej podanih aritmetičnih operacij, ocenimo pa jih glede na njihovo primernosti za klasifikacijo. Slednja temelji na prekrivanju porazdelitve verjetnosti med vrednostmi vzorcev, ki pripadajo različnim razredom. Za nadaljnji razvoj v naslednjo iteracijo izberemo podmnožico najbolj kakovostnih nekoreliranih značilnic z uporabo nove metode, ki temelji na rezu grafa. Pri tem se postopek opira na dva vhoda parametra, ki omogočata nadzor nad številom členov izhodnih značilnic. Prvi opisuje minimalno sprejemljivo kakovost značilnic, ki jih je treba vključiti v izhodni prostor značilnic, medtem ko drugi določa najvišjo dovoljeno stopnjo podobnosti med značilnicama. Rezultati pokažejo, da je metoda nizko občutljiva na
oba vhodna parametra. Naučene značilnice pa statistično značilno izboljšajo klasifikacijsko točnost vseh testiranih klasifikatorjev, medtem ko najboljše točnosti dosežemo z uporabo klasifikatorja naključnih gozdov. Z rezultati primerjave pokažemo, da je predlagani postopek v vseh testnih primerih dosegal ali presegal klasifikacijske točnosti trenutnega stanje tehnike. Prav tako pokažemo tudi pravilnost razlage naučenih značilnic dobro preučene množice testnih podatkov. Keywords: klasifikacija podatkov, razložljiva umetna inteligenca, učenje značilnic, odkrivanje znanja Published in DKUM: 07.05.2024; Views: 266; Downloads: 87
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9. Postopek zaznave sprememb rabe zemljišč s časovno analizo podatkov daljinskega zaznavanja : magistrsko deloSašo Ivič, 2024, master's thesis Abstract: Daljinsko zaznavanje je pridobivanje informacij brez fizičnega stika. S pomočjo satelita Sentinel-2, lahko z odbojem elektromagnetnega valovanja pridobimo informacije o površju Zemlje. Preko platforme Copernicus Open Access Hub, ki jo upravlja Evropska vesoljska agencija, lahko brezplačno dostopamo do podatkov satelitov Copernicus od leta 2015 do danes. V magistrskem delu uporabimo satelitske posnetke satelita Sentinel-2, da strojne modele naučimo prepoznavati spremembe na površju Zemlje, v določenem časovnem obdobju. V prvem delu opišemo tehnike daljinskega zaznavanja sprememb na satelitskih slikah. V nadaljevanju opišemo naši dve metodi implementacije zaznave sprememb in ju primerjamo. Ugotovimo, da obe metodi delujeta dobro in s pomočjo modela nevronskih mrež z visoko natančnostjo zaznavata spremembe na površju. Keywords: Zaznava sprememb, Časovna analiza, Sentinel-2, Daljinsko zaznavanje Published in DKUM: 01.03.2024; Views: 372; Downloads: 34
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10. High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learningMitja Žalik, Domen Mongus, Niko Lukač, 2024, original scientific article Keywords: deep learning, fully convolutional neural network, LiDAR data, digital elevation model, solar energy, solar potential Published in DKUM: 26.01.2024; Views: 244; Downloads: 8
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