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88. Reliefne značilnosti tal in objedanje mladja s strani velikih rastlinojedih parkljarjev v jelovo-bukovem gozduDušan Roženbergar, Robert Klevišar, Jurij Diaci, 2019, original scientific article Abstract: Veliki rastlinojedi parkljarji (VRP) različno vplivajo na razvoj gozdnih ekosistemov. Eden izmed najbolj negativnih vplivov, ki ga imajo na dolgoročni razvoj gozdov, je posledica kroničnega čezmernega objedanja mladja. Namen raziskave je bil s pomočjo analize objedanja v delu dinarskih jelovo-bukovih gozdov ugotoviti, kakšen vpliv na intenzivnost objedanja imajo habitatne značilnosti prostora in relief. Povprečna objedenost mladja je bila 70 %, analiza višinske strukture mladja pa je pokazala, da ni prehajanja gorskega javorja in jelke v zgornje višinske plasti. Potrdili smo vpliv stopnje kritja za VRP in reliefa na stopnjo objedenosti. Največ poškodb smo zabeležili na grebenih in v vrtačah. Glede na rezultate naših analiz v prihodnje v jelovobukovih gozdovih na območju naše raziskave ne bo mogoče zagotoviti primesi jelke in gorskega javorja v zgornjih sestojnih položajih. Če želimo v tem delu Slovenije vzgojiti pestre gozdove, bo poleg gozdno-gojitvenega ukrepanja nujno nadaljevanje intenzivnega gospodarjenja z VRP v smislu zmanjševanja njihovih gostot.
Keywords: objedanje, gojenje gozdov, veliki rastlinojedi parkljarji, relief, pomlajevanje, Abies alba, Fagus sylvatica, Acer pseudoplatanus
Published in DiRROS: 08.07.2019; Views: 7010; Downloads: 2926
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90. 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: 2270; Downloads: 1070
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