E-Locus - Ιδρυματικό Καταθετήριο Πανεπιστημίου Κρήτης - Comparison of different preprocessing and neural network approaches for the investigation of the forecast horizon of time series of wind speed

Αρχική Comparison of different preprocessing and neural network approaches for the investigation of the forecast horizon of time series of wind speed

Αποτελέσματα - Λεπτομέρειες

[Προσθήκη στο καλάθι]

Τίτλος

Comparison of different preprocessing and neural network approaches for the investigation of the forecast horizon of time series of wind speed

Συγγραφέας

Σαρρής, Ηλίας Μάριος

Σύμβουλος διατριβής

Τσιρώνης, Γεώργιος

Μέλος κριτικής επιτροπής

Πανταζής, Γιάννης

Περίληψη

Environmental concerns have encouraged the adoption of renewable energy alternatives to reduce greenhouse gas emissions across the world. As a consequence of that wind farms have been installed in several locations across Greece for the production of wind power. Wind farm management and control, power distribution planning, storage capacity management, and system's dependability, all benefit from reliable wind speed forecasts. In this thesis, the problem of the wind speed forecast has been approached through different preprocessing techniques, that involve scaling, smoothening or data augmentation, as well as Artificial Neural Network models that are based on Gated Recurrent Units (GRUs). Moreover, comparisons were made for different Datasets with consisting of various sampling steps and Datasets, such as Jena Climate, that contain significantly more observations. The comparisons that were made for the preprocessing techniques indicated that the smoothening approach managed to capture more accurately the dynamic structure of the data and perform robust predictions, even for up to 10 – 12 hours ahead. At the same time, the different Neural Network Architectures that were proposed had no significant differences with respect to their performance. Comparison between datasets with different sampling steps (1 hour and 10 minutes) indicated no systematic differences as a result of the stochastic nature of the timeseries. Transfer Learning strategy turned out to behave similarly to the original models for short term predictions, where the long term forecasts appeared to be more robust. Jena Climate dataset, may indicate a potential increase in accuracy of forecasts with the condition that more observations will be added in the datasets at hand.

Γλώσσα

Αγγλικά

Ημερομηνία έκδοσης

2021-07-28

Συλλογή

Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Φυσικής--Πτυχιακές εργασίες

Τύπος Εργασίας--Πτυχιακές εργασίες

Εμφανίσεις

285

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