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Iskalni niz: "avtor" (Gregor Papa) .

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Evolution of electric motor design approaches : the domel case
Gašper Petelin, Gregor Papa, Peter Korošec, 2018

DiRROS - Objavljeno: 06.03.2019; Ogledov: 1805; Prenosov: 637
.pdf Celotno besedilo (840,58 KB)

From a production scheduling simulation to a digital twin
Peter Korošec, Gregor Papa, 2018

DiRROS - Objavljeno: 06.03.2019; Ogledov: 1684; Prenosov: 427
.pdf Celotno besedilo (567,95 KB)

Comparison of multi-objective approaches to the real-world production scheduling
Peter Korošec, Gregor Papa, 2019

DiRROS - Objavljeno: 06.03.2019; Ogledov: 1803; Prenosov: 652
.pdf Celotno besedilo (763,38 KB)

Sensors in proactive maintenance : a case of LTCC pressure sensors
Gregor Papa, Franc Novak, Marina Santo-Zarnik, 2018

DiRROS - Objavljeno: 15.03.2019; Ogledov: 1703; Prenosov: 491
.pdf Celotno besedilo (630,49 KB)

A comparison of models for forecasting the residential natural gas demand of an urban area
Jurij Šilc, Primož Potočnik, Rok Hribar, Gregor Papa, 2019

Povzetek: Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand.
Ključne besede: demand forecasting, buildings, energy modeling, forecast accuracy, machine learning
DiRROS - Objavljeno: 15.03.2019; Ogledov: 1578; Prenosov: 709
.pdf Celotno besedilo (968,06 KB)

Thermal phenomena in LTCC sensor structures
Marina Santo-Zarnik, Franc Novak, Gregor Papa, 2019

DiRROS - Objavljeno: 07.05.2019; Ogledov: 2047; Prenosov: 761
.pdf Celotno besedilo (1,65 MB)

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