Operation and Maintenance Management Based on Machine Learning in Renewable Energy Systems

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.