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Titre : Artificial Intelligence Forecasting Techniques For Reducing Uncertainties In Renewable Energy Applications

Auteur : Alshafeey, Mutaz

Université de soutenance : Corvinus University of Budapest

Grade : Doctor of Philosophy (PhD) 2022

Résumé partiel
The work presented in this thesis provides an integrated view and related insights for solar and wind farm operators and renewable energy regulators regarding factors influencing electricity production using those resources. The findings help production planning and grid stability improvements through better energy forecasting to reduce uncertainty. With the high increase in energy demand expected in both the near and far future, generating energy from green sustainable resources has now become an imperative necessity. Renewable energy sources like wind and solar are among the most promising and environmentally-friendly energy generation options. However, wind and solar energy production are influenced by many variables which affect the reliability, stability, and economic benefits of wind and solar energy projects, therefore, forecasting the potential amount of energy from wind and solar resources is of great importance. Hence, the objective of the work reported here was to explore the possibility of using artificial intelligence methods to accurately predict the generated renewable power from solar and wind farms based on the available data. Specifically, this thesis reports on the following results : 1. At first, solar photovoltaic (PV) energy forecasting was studied. Operators of grid-connected PV farms do not always have full sets of data available to them, especially over an extended period of time as required by key forecasting techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the work reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm), as well as historical weather data (of the same location) with several key variables, were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 minutes resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model : ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques while using the time-series method results in the least accurate forecasting models

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