Veynandt, Francois

Official Name
Veynandt, Francois
Main Affiliation
Akademische Titel
Dr.
Email
francois.veynandt@fh-burgenland.at
Scopus Author ID
55700195500
Status
staff

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    Scopus© Citations 7  1
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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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    DEEP NEURAL NETWORKS FOR OBJECT-DETECTION AND INSTANCE SEGMENTATION OF MECHANICAL, ELECTRICAL AND PLUMBING COMPONENTS: TRANSFER LEARNING ON RADIATORS AS AN EXAMPLE
    (European Council for Computing in Construction, 2025-01-01)
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    Cost estimation and as-built documentation on construction sites require extensive manual work. Combining LiDAR with computer vision offers an effective solution for 5D BIM applications. Therefore, two deep neural networks are trained on a database of over 1000 images of radiators, as example of MEP components. Transfer learning has been applied on SOLOv2 for instance segmentation and on YOLOx for object detection. Given the variety of the image database, the generated models achieve satisfying performance, suitable for the intended applications. The developed models will be used in a scan-to-BIM workflow, enabling the semi-automatic capture of as-built information.
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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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    Measurement dataset from real operation of a hybrid photovoltaic-thermal solar collectors, used for the development of a data-driven model
    (2023-06-21)
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    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.
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    CO2-based occupancy forecasting with an Agent-Based Model
    (IOP Publishing, 2024-06-01)
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    In the realm of building performance optimization, understanding occupancy dynamics is pivotal for enhancing both energy efficiency and occupant comfort. Occupancy forecasts, serving as critical inputs for data-driven predictive control technologies, play a significant role in this domain. To address this need, we propose a novel model that directly estimates building occupancy levels. This model is particularly applicable to buildings equipped with mechanical ventilation systems and CO2 concentration sensors. The number of persons is estimated by utilizing the CO2 production rate of people and applying the principle of mass conservation. The CO2-based approach has been validated with manually recorded ground-truth measurements. A forecast is generated using the first order Markov chain model in combination with an Agent-Based Modell (ABM). The probability transition matrix of the Markov chain defines the behaviour of the occupant-agents, which is used in the ABM to generate behaviour profiles. The model has been tested on four office rooms, with a one-year measurement dataset. The Markov chain with ABM provides a forecast, which encompasses the stochasticity of people's behaviour. The presence True Positive Rate (TPR) reaches 50 % and the False Positive Rate (FPR) is 15 %, in average. The occupancy TPR is only 30 % and the FPR 15 %. The proposed approach offers a framework to easily implement further variables, like occupancy-related power consumption, lighting operation, window opening etc.
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