Jurgen Kocibelli
Machine Learning techniques to forecast energy production from Wave Energy Converters.
Rel. Edoardo Patti, Rafael Natalio Fontana Crespo. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2025
Abstract
Wave energy has recently emerged as a stronger contender when it comes to renewable energy sources. With its great potential come a few drawbacks, such as the intermittent and non-stationary nature of the sea waves, where just like other renewable energy sources their power fluctuates and makes it challenging to have it integrated into the grid. It is of great importance to predict the output power of this energy source to ensure its full integration into the smart grid. This thesis evaluates the effectiveness of employing exogenous inputs for forecasting the power output in short term horizons of 15 to 240 minutes of the Inertial Sea Wave Energy Converter (ISWEC).
Wave energy is captured from the ISWEC using the inertial effects of a gyroscope
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