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Query: "author" (Rok Hribar) .

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1.
Models for forecasting the traffic flow within the city of Ljubljana
Gašper Petelin, Rok Hribar, Gregor Papa, 2023, original scientific article

Abstract: Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.
Keywords: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison
Published in DiRROS: 28.09.2023; Views: 299; Downloads: 128
.pdf Full text (5,05 MB)
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2.
On suitability of the customized measuring device for electric motor
Rok Hribar, Gašper Petelin, Margarita Antoniou, Anton Biasizzo, Stane Ciglarič, Gregor Papa, 2022, published scientific conference contribution

Published in DiRROS: 13.12.2022; Views: 437; Downloads: 156
.pdf Full text (249,35 KB)

3.
Construction of Heuristic for Protein Structure Optimization using deep reinforcement learning
Rok Hribar, Jurij Šilc, Gregor Papa, 2018, published scientific conference contribution

Published in DiRROS: 15.03.2019; Views: 2370; Downloads: 1136
.pdf Full text (510,93 KB)

4.
A comparison of models for forecasting the residential natural gas demand of an urban area
Rok 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: 2236; Downloads: 1060
.pdf Full text (968,06 KB)

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