Forecasting river discharge using machine learning methods: with application to the Geul and Rur river

The objective of this study is find out whether maximum daily discharge of the Geul and Rur catchments can be forecast using machine learning (ML) methods, and if so, to what extent. In addition, these ML models are compared to a conceptual model to see which performs better. A second objective is to test whether soil moisture content (SMC) and...

Photovoltaic Yield Nowcasting: For Residential Solar Systems in the Netherlands Using a Machine Learning Approach

An increasing number of photovoltaic (PV) systems are being installed worldwide and residential sector is responsible for a large part of this growth. Small scale PV systems do not have complex measuring devices and their breakdowns are not spotted immediately by the system owners. This might lead to prolonged time without generating power and...

Investigation of machine learning techniques for wind turbine fault detection using experimental data

Increasing awareness about climate change and increasing interest in renewable energy is fueling the rise of wind energy. One of the main challenges currently faced by the wind energy industry is to improve the reliability and availability of wind turbine in order to keep the industry financially attractive. Optimising operations and maintenance...