Gnam, Lukas

Official Name
Gnam, Lukas
Main Affiliation
Jobtitel
Hochschullehrender, Kollegiumsmitglied
Web Site
Email
lukas.gnam@hochschule-burgenland.at
ORCID
Scopus Author ID
57202650961
OpenAlex ID
A5055126003

Hochschulpersonal

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    Selbstlernendes Empfehlungssystem zur Steigerung der Behaglichkeit
    (Leykam, 2019-11)
    Rabensteiner, Markus 
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    Pratter, Robert 
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    Within the project „Empower Citizens” technical as well as social and health aspects are combined in order to increase the subjective comfort inside a flat with respect to room temperature, CO2 concentration, humidity, and so forth. Therefore, a low-cost system advisory system is developed which reads the aforementioned data from sensors situated in the different rooms of a flat. Using these measurements advices are given in order to increase the resident’s comfort. The advices are based on the computation of the so-called predicted mean value (PMV) which aims at capturing the comfort with respect to different parameters, e.g., age, body weight, and height. To achieve this goal, first, a co-simulation using Matlab and IDA-ICE was conducted utilizing a detailed model of the investigated flat. The model is split into two different zones, living room and bedroom, where a zone is defined through the presence of dedicated sensors inside the zone. The sensor data are transmitted to a central station, which reads also outside temperature and humidity, and form the basis for the PMV computation. If the PMV exceeds pre-defined limits, an advice is given, either to open a window or the shadow a window. Additionally, an advice is output if the CO2 concentration is too high. Furthermore, the advisory system is capable of learning how well the given advices are followed by the resident. Thus, the total number and the times at which advices are output can be flexibly adjusted by the system itself to optimally suit the resident’s fondness for following the advices. To evaluate the developed advisory system a parameter study was conducted evaluating different reference cases with respect to the parameters affecting the PMV calculation, e.g., age and bodyweight. Additionally, different motivations to follow the given advices were modeled within these reference cases. The simulation results prove that following the advices given by the advisory system leads to increased comfort for the residents while keeping the increase in energy demand for heating occurring from more ventilation to a minimum. Additionally, the study shows that ventilation only in the morning results in high CO2-concentrations heavily influencing the resident’s comfort. urthermore, this study presents a first easy-to-install hardware prototype comprised of a RaspberryPi 3B+ and an ARDUINO MKR1000 where the developed algorithm for the advisory system can be deployed. This prototype includes also the required sensors to monitor room temperature and CO2 concentration. In order to output the computed advices, it is equipped with an LCD display acting as human machine interface.
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    Langfristige Prognose für den Wärmebedarf eines Nahwärmenetzes unter Berücksichtigung demografischer Entwicklungen
    (2020-02)
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    In dieser Arbeit wird ein Modell zur Prognose des Fernwärmebedarfs von Nahwärmenetzen für die Jahre 2030 und 2050 unter Berücksichtigung von Klima- und demografischen Entwicklungen vorgestellt. Dabei wird mit einer leicht rückläufigen Entwicklung der Bevölkerungszahlen, einer steigenden Anzahl an Wohnungen und Gebäuden insgesamt einem Anstieg der Beschäftigten in der Industrie gerechnet. Die Ergebnisse zeigen eine generelle Zunahme der notwendigen Wärmeenergie, eine erhöhte Spitzenleistung im Szenario 2050 sowie einen deutlich erhöhten Wärmebedarf für Kühlung im Sommer unter der Annahme der Nutzung sorptionsgestützter Kühlung. Im Falle der Modellregion ist die Wärmeversorgung mit der bestehenden Infrastruktur bis ins Jahr 2030 gesichert, darüberhinausgehend sind jedoch Erweiterungen erforderlich.
      572  174
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    Forecasting and Optimization Approaches Utilized for Simulating a Hybrid District Heating System
    (Hochschule Luzern, 2020-10)
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    The historically grown centralized energy system is undergoing massive changes due to the transformation from centralized energy production with large assets (e.g. fossil-thermal power plants) towards a sustainable, clean and decentralized energy system. This transformation is based on the inclusion of renewable energy sources (RESs) (e.g., wind and solar) into the classical systems. However, as the energy production stemming from RESs is extremely volatile and thus challenging to predict, new approaches have to be found in order to guarantee a successful integration of RESs into the existing infrastructure. In the Austrian state of Burgenland approximately 1,000 MW of wind capacity is available. As already mentioned above, the high volatility of wind energy together with forecast uncertainties hinders the optimal integration of this RES into the existing energy system. Furthermore, the successful deployment of wind turbines was based on an attractive but timely limited subsidy scheme with a fixed feed-in tariff. As these subsidies now come to an end for more and more wind turbines and future support systems will rely on market premiums and tendering models, new approaches and business models have to be devised in order to sustain the rapid transformation of the classical energy systems. In the research project HDH Demo in close cooperation with the city of Neusiedl am See, Burgenland, Austria, the aim is to integrate wind energy into the existing district heating grid of the city. This is realized by utilizing power-to-heat technologies, e.g., heat pumps. However, an economically feasible and successful integration is based on accurate forecasts for both, wind production and district heating demand as well as the actual energy prices. Therefore, this work evaluates the applied data-driven forecasting methods. In particular, ensemble approaches that combine autoregressive models with artificial intelligent techniques are used to exploit the strengths of different methods (e.g. stability, flexibility). To compare the model performance, an overview on the accuracy and efficiency of the ensembles by using appropriate score metrics (e.g. RMSE, MAPE, R²) is given. Furthermore, a mixed integer linear optimization model is presented for computing optimized schedules for the different components (e.g., heat pumps, energy storage units, biomass boiler) of the district heating grid. Together, these two approaches, forecasting and optimization, are used to investigate and evaluate different business models, which help to ensure the future market integration of wind production.
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    Open Innovation Processes as Drivers for Business Model Developments Enabling a Successful Energy Transition
    (2020-09)
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    Renewable energy systems are a key enabler for a successful transition of our current energy systems. However, in every transition process numerous different interests and requirements, often diverging or opposed to each other, must be considered. “Open Innovation” approaches overcome this divergency and increase one’s own innovation potential by involving all relevant actors along the value chain. This work presents approaches, experiences, challenges, and results of Open Innovation processes applied to stakeholder groups in three different use cases. In these use cases, multi-stage, iterative processes for stakeholder integration are applied. The results of the first use case stages show a high degree of diversity in terms of different stakeholder treatment. These conflicts are tackled by the Open Innovation process, proving that a fruitful collaborative business model development is feasible.
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    An interdisciplinary approach of a local peer-to-peer energy trading model for a more sustainable power grid
    (SDEWES Centre, 2021-10)
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    This work investigates a novel distance-based electricity tariff applicable to energy communities. Based on a mixed integer linear optimization model computational studies are conducted to investigate the effects of this tariff on a small-scale community consisting of 22 consumers and 5 prosumers. Additionally, the potential of electric storage systems supporting the use of locally produced energy is investigated. The results of the computational model prove that an energy community generates incentives photovoltaic plants and the installation of energy storage systems. Complementary to the simulations, the flexibility options are supported by a user survey focusing on technology attributes for local energy market models, proving that there is a significant awareness for the use of energy storage systems.
      570  140
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    Preferences on Home Energy Management Systems - A Study of Potentials for the Use of End User Flexibilities
    (2020-09)
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    Due to the increase of volatile renewable energy resources, additional flexibility in the electricity system is necessary to ensure a technically and economically efficient network operation. To tap flexibilities like private storages, participatory developed and user-friendly technical solutions are essential. In this work, the acceptance and the market potential of a home energy management system with focus on i) grid optimization, ii) self-consumption optimization, and iii) additional functions for increased comfort is presented. This is determined by means of a representative survey. The main results show that the perceived usefulness and accessibility are the most important influence factors affecting the end user’s intention to use. Based on the given acceptance, the solution provides a market potential of one third of the users for an increased use of flexibilities in the investigated area.
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