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Veynandt, Francois
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Official Name
Veynandt, Francois
Main Affiliation
Akademische Titel
Dr.
Email
francois.veynandt@fh-burgenland.at
Scopus Author ID
55700195500
Status
staff
Research Outputs
Now showing 1 - 7 of 7
- ProductMeasurement dataset from real operation of a hybrid photovoltaic-thermal solar collectors, used for the development of a data-driven model(2023-06-21)
; ; ; This dataset contains measurements from real operation of a hybrid photovoltaic-thermal solar collector. The data is from a summer period, when the collector works at its higher temperature limit, with low thermal efficiency. The dataset characterizes the output of the collector: thermal (heat transfer fluid flowrate, inlet and outlet temperatures) and electrical (raw current and voltage, Hampel filtered power). Further information on the collector are the PV cell temperature and the back surface temperature (in three points). Detailed weather information are included: ambient temperature, solar resource (direct normal, global and diffuse horizontal, global tilted in the collector plane), equivalent radiative sky temperature (calculated from a pyrgeometer), wind speed and direction both horizontal and in the tilted collector plane. The calculated sun position with Duffie and Beckmann method is also given (elevation and azimuth) . The dataset covers 58 summer days from 11th July to 6th September, with a 5 second time step. The data is available as .mat file (MATLAB) and .csv file. This dataset is presented in details in a dedicated article [1]. A selection of variables from this dataset has already been used in the development of a data-driven model [2]. References: [1] F. Veynandt, F. Inschlag, C. Seidl, C. Heschl, Measurement data from real operation of a hybrid photovoltaic-thermal solar collectors, used for the development of a data-driven model, Data in Brief. 49 (2023) 109417. https://doi.org/10.1016/j.dib.2023.109417. [2] F. Veynandt, P. Klanatsky, H. Plank, C. Heschl, Hybrid photovoltaic-thermal solar collector modelling with parameter identification using operation data, Energy and Buildings. 295 (2023) 113277. https://doi.org/10.1016/j.enbuild.2023.113277.201 77 - PublicationMonitoring data from an office room in a real operating building, suitable for state-space energy modelling(2024)
; ; ; The dataset provides all necessary variables for data-driven energy modelling of an office room. The measurement data have been obtained from an office building operating as living lab in a temperate climate of Central Europe. The temperatures and the ventilation air flowrate are raw measurements, while the heat flows are calculated from measurements. The incoming solar irradiance is calculated with two façade models –simple and enhanced–, using measurements (solar irradiance, movable shading settings) and building characteristics (geometry, glazing and shading properties). One year and four months of data is provided with a fine one-minute time step and a coarser fifteen-minute time step. The dataset can be used to test and validate data-driven models, for example for predictive control applications.14 113 - PublicationModeling of solar radiation transmission through triple glazing based only on on-site measurements(2020)
; A model is developed which can calculate the irradiance behind a glazed façade, based on measured incident solar radiation. Data from a real office building is used to establish and test the model. Onsite irradiance measurements on both sides of the facades are exploited to calibrate the model. The main parameters are the average diffuse transmittance and a correlation for the direct transmittance, as a function of the incidence angle. Correction of the measurements, including the frame reflection, are taken into account. Hence, the model contributes to the solar heat flux identification, in the perspective of model predictive control application.25 136 - ProductMonitoring dataset from an office room in a real operating building, suitable for state-space energy modelling(2023-07-21)
; ; ; 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/10.3217/978-3-85125-786-1-03.155 98 - PublicationMeasurement data from real operation of a hybrid photovoltaic-thermal solar collectors, used for the development of a data-driven model(2023)
; ; ; This article presents a measurement dataset from real operation of a hybrid photovoltaic-thermal solar collector. The data is from a summer period, when the collector works at its higher temperature limit, with low thermal efficiency. The dataset characterizes the output of the collector: thermal (heat transfer fluid flowrate, inlet and outlet temperatures) and electrical (raw current and voltage, Hampel filtered power). Further information on the collector are the PV cell temperature and the back surface temperature (in three points). It provides detailed weather information: ambient temperature, solar resource (direct normal, global and diffuse horizontal, global tilted in the collector plane), equivalent radiative sky temperature (calculated from a pyrgeometer), wind speed and direction both horizontal and in the tilted collector plane. The calculated sun position with Duffie and Beckmann method is also given (elevation and azimuth) . The dataset covers 58 summer days from 11th July to 6th September, with a 5 second time step. The data is available as .mat file (MATLAB) and .csv file. A selection of variables from this dataset has already been used in the development of a data-driven model (see related article) [1]. The extended data presented in this article offers mode detailed weather information, opening further investigations opportunities. Further options for data-driven modelling of PVT collectors could be investigated. The correlation of wind related losses to horizontal wind measurements could be compared to a model with wind measurements in the collector plane. The dataset could support the validation of solar models, with direct and diffuse shares on the horizontal or in the tilted plane. [Duck Duck Go](https://duckduckgo.com)67 552 - Publication
42 1134Scopus© Citations 2 - PublicationComplex glass facade modelling for Model Predictive Control of thermal loads: impact of the solar load identification on the state-space model accuracyAbove 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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