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1.
Effects of specific parameters on simulations of energy use and air temperatures in offices equipped with radiant heating/cooling panels
Sabina Jordan, Jože Hafner, Martina Zbašnik-Senegačnik, Andraž Legat, 2019, izvirni znanstveni članek

Povzetek: When creating a simulation model to assess the performance of buildings, there is usually a lack of feedback information. Only in the case of measurements of a real building is a direct comparison of the measured values and simulated results possible. Parameter data related to users’ behavior or other events can also be obtained. Their evaluated frequency, magnitude and duration, along with boundary conditions, are crucial for the results. It is clear that none of them can be predicted very accurately. Most of them, however, are needed for computer modeling. In this paper we analyzed the well-defined TRNSYS simulation model of offices equipped with radiant ceiling panels for heating and cooling. The model was based on real case offices and was validated based on measurements for 1 year. The analysis included simulations in order to define what effect the parameters related mainly to users have on the energy use and the indoor air temperatures. The study confirmed that specific human activities influence the annual energy use to a relatively small degree and that their effects often counteract. It also confirmed the even more important fact that although small, these activities can influence the thermal comfort of users. It is believed that despite the fact that this research was based on an analysis of offices equipped with radiant ceiling panels, most of the results could be applied generally.
Ključne besede: measurements, modeling, simulation, validation, analysis, energy use, temperature
Objavljeno v DiRROS: 15.09.2023; Ogledov: 243; Prenosov: 121
.pdf Celotno besedilo (1,83 MB)
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2.
A comparison of models for forecasting the residential natural gas demand of an urban area
Rok Hribar, Primož Potočnik, Jurij Šilc, Gregor Papa, 2019, izvirni znanstveni članek

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
Objavljeno v DiRROS: 15.03.2019; Ogledov: 2237; Prenosov: 1060
.pdf Celotno besedilo (968,06 KB)

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