Operation and Maintenance Management Based on Machine Learning in Renewable Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 3324
Special Issue Editor
Special Issue Information
Dear Colleagues,
Renewable energies are gaining importance in the global energy production capacity. Climate change, pollution, and other problems associated with fossil fuels make renewable energy systems critical for the survival of the world civilization. This absolute necessity and the emergence of new technologies are causing an important increase in the size and complexity of modern renewable energy systems.
Modern systems require more components and subsystems, and therefore, faults’ frequency and failure mechanisms usually increase. Nevertheless, the reliability, availability, maintainability, and safety of systems must not be worsened by the modernization process. For this purpose, operation and maintenance (O&M) management plays an essential role. In other words, progress in renewable energy systems must be accompanied by progress in O&M techniques.
Today, the acquisition and processing of data from these systems is becoming increasingly important to ensure a correct operation. A proper data processing can provide valuable information for discovering, forecasting, or correcting faults, abnormal behaviors, or bad system conditions. In this field, machine learning algorithms have been demonstrated to be a powerful tool. Moreover, they can be employed for building efficient and cost-effective O&M policies with a subsequent improvement of system performance. In general, machine learning algorithms facilitate a smarter data-driven decision-making process.
The main goal of this Special Issue is to publish high-quality articles that contribute to O&M management of renewable energy production systems using machine-learning-based methods. New machine learning models, including deep-learning-based models, novel approaches or case studies with existing algorithms applied to any type of renewable energy will be considered for publication. Reviews of O&M management in renewable energy systems renewable energy will also be considered. In general, papers joining machine learning and renewable energy will be considered for publication.
Dr. Alberto Pliego Marugán
Guest Editor
Manuscript Submission Information
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Keywords
- Renewable energy systems
- Operation and maintenance
- Maintenance management
- System reliability
- Operational research
- Machine learning
- Deep learning
- Decision making
- Decision support system
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Artificial Intelligence
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