311. A comparison of models for forecasting the residential natural gas demand of an urban areaRok Hribar, Primož Potočnik, Jurij Šilc, Gregor Papa, 2019, original scientific article Abstract: 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. Keywords: demand forecasting, buildings, energy modeling, forecast accuracy, machine learning Published in DiRROS: 15.03.2019; Views: 2333; Downloads: 1087 Full text (968,06 KB) |
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317. Smernice za diagnostiko, zdravljenje in sledenje bolnikov s sarkomi mehkih tkiv in kostiMarko Novak, Olga Blatnik, Jože Pižem, Tina Žagar, Monika Jagodic, Lorna Zadravec-Zaletel, Mojca Unk, Blaž Mavčič, Jerca Blazina, Snežana Pavlović Djokić, Darja Eržen, Simona Jereb, Andreja Klevišar Ivančič, Gregor Kos, Mateja Kropivnik, David Martinčič, Eva Možina, Andraž Perhavec, Marija Skoblar Vidmar, Uroš Smrdel, Marko Špiler, Danijela Štrbac, Branko Zakotnik, Katja Limpel Novak, Vladka Salapura, Žiga Snoj, 2018, professional article Keywords: sarkomi mehkih tkiv, sarkomi kosti, diagnostika, zdravljenje Published in DiRROS: 06.02.2019; Views: 3243; Downloads: 987 Full text (278,51 KB) |
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