Derler, Bernhard

Official Name
Derler, Bernhard
Scopus Author ID
57203592245

Ehemaliger MA

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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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    Getting Fit for the Future: Optimizing Energy Usage in Existing Buildings by Adding Non-Invasive Sensor Networks
    (IEEE, 2018-08)
    Sauter, Thilo 
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    Treytl, Albert 
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    Diwold, Konrad 
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    Molnar, David 
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    Lechner, Daniel 
    Optimizing energy usage is becoming an economic necessity for existing buildings. Non-invasive sensors and sensor networks are key technologies for efficiently achieving this goal, since it is of utmost importance that existing hydraulic systems are not changed and the engineering effort for installation remains minimal. This paper presents a data-driven approach that should allow low-cost installation of sensors at arbitrary points of the building and then retrieve the structure of the hydraulic system from the recorded sensor values. The architecture as well as first preliminary results from field test buildings are presented.
      1Scopus© Citations 1  407
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    Towards non-invasive temperature measurements in HVAC: A characterization and correction approach
    (IEEE, 2019-06)
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    Sauter, Thilo 
    The existing building stock within the European Union is responsible for a considerably huge amount of the total energy consumed. This has prompted legislative actions that focus on increasing the efficiency of Heating, Ventilation and Air Conditioning facilities by employing building automation and electronic monitoring systems. The fluid flow temperature in the hydraulic grid of a building is therefore an essential parameter to be measured, where clamp-on temperature sensors are often applied due to their simple and cost-effective installation. As the plumbing industry heads towards non-metal pipe materials with low thermal conductivity, the applicability of non- invasive measurement procedures diminishes. In this context, a characterization approach of non-invasive temperature measurements that is linked to a thermal resistance model is experimentally validated. Based on that, a correction algorithm to reduce the deviation between measured surface and the fluid flow temperature for steady state conditions is derived and tested. The presented approach provides sufficient characterization and correction performance, albeit several limitations have to be taken into consideration.
      1Scopus© Citations 1  421