Modelling and Simulation of Systems
Topics of the track
Assisting PV experts in on-site condition evaluation of PV modules using weather-independent dark IV string curves, artificial intelligence and a web-database
Photovoltaic (PV) modules can make a huge contribution to achieve the Sustainable Development Goals of the United Nations. To be able to make that contribution, regular check-ups and evaluation of installed PV modules are necessary as they can develop faults and degenerate over time. In this project, we improve the *dark IV string curve method* used for on-site fault detection and module evaluation. We do so by training artificial intelligence (AI) models to predict the maximum power point and the bright IV curve of PV modules given the weather-independent dark IV string curve. We present some background on this topic, describe the data used for training and the developed models. The results are illustrated graphically. To make the models available for PV experts in practice and to support their decision-making process, we also developed the web-database-application *iPVModule* for storing historical PV Module data and integrated the AI-models.
Air pollution due to central heating of a city-centered university campus
The aim of this study was to determine the gaseous pollutant concentrations re-sulting from the natural gas-powered central heating at the Aristotle University of Thessaloniki main campus, on the basis of limited emission and meteorological data. For this reason, a methodology was compiled addressing emissions and concentration levels as a function of a set of meteorological scenarios: Emissions were estimated based on campus operational conditions and concentration levels were calculated by employing the Gaussian plume model approach via an in-house implementation in Python. The necessary climatic conditions were used to compile a total of 1080 different weather – dispersion model simulation scenari-os. The obtained results allowed the adequate analysis of the geospatial distribu-tion of pollutants. Concentration levels were estimated to be below relevant limit values but dependent on the prevailing meteorological conditions.
EpiDesktop - A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios
Human mobility has been recognized as one of the critical factors of contagious diseases spread. SARS-CoV-2 as a highly contagious and eluding virus is not an exception affecting the normal lives of more than half of the global population in a way or another and claiming the lives of hundreds of thousands. As a response to such a situation, mobility should be managed by imposing certain policies. In light of this, this proposed study presents a newly developed GIS platform aiming at simulating and mapping the spread of infectious diseases and the mobility patterns under different scenarios based on different epidemiological models. In addition to the "business as usual" scenario, other response scenarios can be defined to reflect real-world situations taking into consideration various parameters including the daily rise of infected and deaths, among others. The developed system might offer a useful tool for decision-makers for insights about strategies to be implemented and measures to control the spread of the virus.
Chair: Grit Behrens