Editorial: Advanced data-driven methods for monitoring solar and wind energy systems

Renewable energy systems, specifically wind and solar photovoltaic (PV) systems, play a crucial role in addressing the urgent need for sustainable and reliable energy sources. They reduce dependence on fossil fuels and contribute to the fight against climate change. Additionally, the growth of these systems leads to the creation of jobs and economic growth. They provide a sustainable energy source that can be harnessed for decades to come. Furthermore, by diversifying energy sources they improve energy security, and they help to promote energy access, particularly in rural and remote areas, and reducing poverty.

The integration of renewable energy systems such as wind turbines and PV systems into the power grid requires accurate monitoring and prediction of their power production. This is necessary to ensure that the energy supply matches the demand and to avoid power outages and blackouts. Predictive maintenance and fault detection also play a critical role in ensuring the optimal performance and longevity of these systems, which can reduce costs and minimize the environmental impact.

Advanced data-driven methods can be used for monitoring, modeling, and fault detection, improving the prediction accuracy and overall performance of these renewable energy systems and supporting the integration of renewable energy in the power grid.

Artificial Intelligence (AI) methods such as machine learning and deep learning have a critical role in monitoring and optimizing solar PV and wind energy systems. These methods can analyze large amounts of data from the systems and identify patterns and trends that are not immediately apparent to humans. This can help in monitoring and optimizing the performance, reliability, and efficiency of these systems and also identifying faults and predicting power production. This Research Topic invited contributions that address wind turbine and PV systems faults and power prediction through innovative applications and novel contributions. It covers topics such as fault detection and diagnosis, power prediction, condition monitoring, deep learning, machine learning, and data-based methods for monitoring and optimizing solar PV and wind energy systems.

After rigorous review, four high-quality articles contributed by 16 authors were finally accepted for their contributions to the topic.