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Αρχική    Forecasting land-atmosphere boundary temperatures using deep neural networks at regional scales over long temporal periods  

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Τίτλος Forecasting land-atmosphere boundary temperatures using deep neural networks at regional scales over long temporal periods
Άλλος τίτλος Πρόγνωση θερμοκρασίας στη διεπαφή ατμόσφαιρας-γής με χρήση τεχνικών βαθιάς μάθησης σε περιφερειακό επίπεδο για μεγάλες χρονικές περιόδους
Συγγραφέας Σουκαράς, Αθανάσιος
Σύμβουλος διατριβής Τσακαλίδης, Παναγιώτης
Περίληψη If the COVID-19 pandemic is any indication is that science’s unique approach can provide solutions to the most dire situations. That approach is much needed when it comes to sustainability matters. Planet sustainability is shaping up to be ”the” problem of the 21st century and it has never been more prevalent among the scientific community due to its high complexity. Sustainability is a social, economic, political and environmental problem. One of the focal issues surrounding a sustainable ecosystem is its energy dependence. From production to consumption and from storage to distribution, energy is a resource solely dependent on how it’s utilized by us. Renewable Energy Sources (RES) have shown the potential to relieve energy demands by providing, clean, green energy but due to the nature of renewable energy, the energy output from such sources is inconsistent and an active topic among the scientific community. Now casting weather phenomena and forecasting long term climate variability and essential variables like surface land temperature, has been well documented. Layers of complexity are added when the interest of research is towards providing knowledge over temporal periods of months. To further facilitate this research we used deep neural networks (DNN). Deep learning architectures have revolutionized many scientific domains due to their superior performance and flexibility by being able to extract aggregated knowledge when enough training data is available. In this thesis we utilized the state of the art Convolutional Long - Short Term Memory (ConvLSTM) architecture in order to forecast land - atmosphere boundary temperatures at the region of Crete over the span of months. We explored three different bands, temperature of air at 2 meters, skin temperature and soil temperatures between 0 and 7 cm below surface level provided by ERA-5 Land, which is a climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 providing a consistent view of the evolution of land variables over several decades. We explored the different cases surrounding the time horizon of the forecast. In the first case we explored our models performance when we predicted the afore- mentioned bands a month ahead. Such a prediction gathers not only scientific and environmental interest but interest from the general populous too. It can provide invaluable information about energy consumption and grid on - off grid stability. In the second case we look to further our research by providing knowledge for longer horizons. We present results that account for 6 months ahead. By doing so we provide information that can be used for RES resources allocation betterment of policy making and more sustainable and efficient energy consumption if the need arise. In the third case we explored how subtracting data after the training and fit routine can affect future predictions in the span of one, two and three months. This study revealed how a limitation on previous knowledge can significantly affect future observations.
Γλώσσα Αγγλικά
Ημερομηνία έκδοσης 2022-07-22
Συλλογή   Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Φυσικής--Πτυχιακές εργασίες
  Τύπος Εργασίας--Πτυχιακές εργασίες
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