Klanatsky, Peter

Official Name
Klanatsky, Peter
Main Affiliation
Akademische Titel
DI(FH)
Email
peter.klanatsky@hochschule-burgenland.at
ORCID
Scopus Author ID
39461478500
Status
staff

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    Numerical simulation of airflow around buildings
    (Leykam, 2015-11-26)
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    Dragosits, T. 
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    Exponential pattern recognition for deriving air change rates from CO2 data
    (Institute of Electrical and Electronics Engineers (IEEE), 2017)
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    Mateis, Cristinel 
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    Dejan, Nickovic 
    A novel procedure for automated determination of air change rates from measured indoor CO2 concentrations is proposed. The suggested approach builds upon a new algorithm to detect exponential build-up and decay patterns in CO2 concentration time series. The feasibility of the concept is proved with a test run on synthetic data that shows a good reproduction of the previously defined air change distribution. The demonstration continues with test runs on CO2 datasets measured in the kitchen and the sleeping room of two residential buildings. The derived air change rates were within the expected distributions and ranges in both cases when natural or mechanical ventilation was used.
    Scopus© Citations 3  300  668
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    Complex glass facade modelling for Model Predictive Control of thermal loads: impact of the solar load identification on the state-space model accuracy
    (Leykam, 2020-11-26)
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    Above and beyond improving the efficiency of the building envelope and the energy supply system, the demand-side flexibility in terms of load shifting and peak reduction are vital factors in further increasing the share of volatile renewable energy sources. The thermal activation of building components, like floors and ceilings, enables the cost-effective potential for short-term energy storage to fulfil these requirements. In order to exploit the storage capabilities of active building systems, a reliable model predicted control (MPC) approach is required. However, primarily if a large glass façade element is utilised, the appropriate modelling of solar loads is critical for an effective MPC operation. Hence, based on a dynamic building simulation tool, a characteristic map for the solar load prediction of a glass façade system in combination of external venetian blinds was generated to enhance the state-space model approach for the MPC algorithm. The comparison with a conventional state-space model approach shows the integration of a detailed characteristic map can only marginally improve the prediction accuracy. The additional information required from the glass façade manufacturer and the associated simulation effort is not of substantial value. In contrast, the conventional grey box model enables an entirely datadriven parameter identification, without the manufacturers’ data. Furthermore, the MPC optimisation procedure, searching for the best control strategy, can be more efficient (solver-based optimisation), with shorter computing turnaround times.
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    Monitoring dataset from an office room in a real operating building, suitable for state-space energy modelling
    (2023-07-21)
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    To support open science, monitoring data from the living laboratory ENERGETIKUM in Pinkafeld, Austria, is shared here. The dataset provided is especially suitable for data-driven energy modelling of an office room. This can be for model predictive control strategies useful. The dataset provides all necessary variables over a period of sixteen months, with a time step of one minute or fifteen minutes, in MATLAB format (.mat) or in tabs-separated format (.txt). Some variables are raw measurements: ambient (T_Amb) and room (T_Air_Measured) air temperatures, ventilation air flowrate (V_dot_Vent) and supply temperature (T_Vent_In). Other variables are calculated from measurements: heat flows for floor heating (Q_dot_FBH), ceiling cooling (Q_dot_DE) and from internal loads (Q_dot_Int_LO). For the incoming solar irradiance, two façade models using measurements (solar irradiance, movable shading settings) and building characteristics (geometry, glazing and shading optical properties) are used: the simple model (q_dot_Solar_SF) and the enhanced model (Q_dot_Solar_EF). To the background of the façade models, see [1,2]. References: [1] F. Veynandt, C. Heschl, P. Klanatsky, H. Plank, Complex glass facade modelling for Model Predictive Control of thermal loads: impact of the solar load identification on the state-space model accuracy, Leykam, 2020. http://hdl.handle.net/20.500.11790/1396 (accessed January 31, 2022). [2] Veynandt, F., Heschl, C., MODELING OF SOLAR RADIATION TRANSMISSION THROUGH TRIPLE GLAZING BASED ONLY ON ON-SITE MEASUREMENTS, in: Verlag der Technischen Universität Graz, Online Conference, 2020. https://doi.org/10.3217/978-3-85125-786-1-03.
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