About this Research Topic
This Research Topic is part of a series: Volume I
The fast evolution of the renewable energy market has dramatically increased the demand for photovoltaic systems and wind turbines. However, photovoltaic power production decreases when faults are introduced in the photovoltaic system. Appropriately detecting and identifying faults in a photovoltaic plant are indispensable to maintain the generated power at the desired level.
For decades, challenges are proposed to operators. Updated instrumentation, control, and automation are offering overwhelmingly voluminous data, which are often unexploited, forming the data-rich, information-poor dilemma. Timely analysis and modeling would extract valuable information to support process understanding, online prediction, and process monitoring and predictive control.
This Research Topic is motivated by the requirements posed in the specifications of the advanced renewable energy systems (wind and solar), and new relevant concepts where machine learning can potentially be a true enabler. Thus, this call seeks submissions on innovative data-driven methods for modeling and monitoring renewable energy systems, with particular focus on solar and wind energy.
Potential topics include but are not limited to:
• Automatic supervision
• Machine Learning and Deep Learning for solar and wind technology
• Ensemble learning for renewable energy systems
• Multivariate statistical process monitoring techniques for PV systems and wind turbines
• Wind and solar power forecasting
• Deep Reinforcement Learning for renewable energy systems
• Machine learning algorithms for anomaly detection/identification in PV systems
• Statistical fault detection and isolation
• On-line and off-line fault detection
The fast evolution of the renewable energy market has dramatically increased the demand for photovoltaic systems and wind turbines. However, photovoltaic power production decreases when faults are introduced in the photovoltaic system. Appropriately detecting and identifying faults in a photovoltaic plant are indispensable to maintain the generated power at the desired level.
For decades, challenges are proposed to operators. Updated instrumentation, control, and automation are offering overwhelmingly voluminous data, which are often unexploited, forming the data-rich, information-poor dilemma. Timely analysis and modeling would extract valuable information to support process understanding, online prediction, and process monitoring and predictive control.
This Research Topic is motivated by the requirements posed in the specifications of the advanced renewable energy systems (wind and solar), and new relevant concepts where machine learning can potentially be a true enabler. Thus, this call seeks submissions on innovative data-driven methods for modeling and monitoring renewable energy systems, with particular focus on solar and wind energy.
Potential topics include but are not limited to:
• Automatic supervision
• Machine Learning and Deep Learning for solar and wind technology
• Ensemble learning for renewable energy systems
• Multivariate statistical process monitoring techniques for PV systems and wind turbines
• Wind and solar power forecasting
• Deep Reinforcement Learning for renewable energy systems
• Machine learning algorithms for anomaly detection/identification in PV systems
• Statistical fault detection and isolation
• On-line and off-line fault detection
Keywords: Solar and wind energy systems; Fault detection and diagnosis; Wind and solar power forecasting; Deep learning and machine learning; Data-driven methods., #CollectionSeries
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
