Ivan D'Onofrio
Spatiotemporal Graph Neural Networks for Wind Energy Production Forecasting.
Rel. Paolo Garza, Luca Colomba. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
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Abstract
Over the past few decades, global energy consumption has steadily increased, driven by population growth, industrialization, and technological advancements. In response to the growing energy demand and the shift towards sustainable power generation, wind energy has gained significant attention due to its environmental benefits and economic viability. The main element involved in wind energy production is the wind turbine, a device that converts the kinetic energy of wind into electrical energy relying on the principle of electromagnetic induction. However, due to the inherent variability of wind patterns and environmental conditions, wind energy production is characterized by a dynamic output, which poses operational challenges for its integration into the power grid.
In this context, predictive modeling of wind energy output plays a relevant role in supporting the dynamic management of wind operations, enabling strategic demand allocation and optimized use of energy storage
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