Advances in AI Methods for Wind Power Forecasting and Monitoring

Dear Colleagues,

This Special Issue aims to combine the latest research on AI methods for forecasting and monitoring wind power. Wind power has become an essential part of the global energy mix, and accurate forecasting and monitoring of wind power production are crucial for efficient energy management and grid stability. However, wind power forecasting and monitoring are challenging due to the high dimensional and intermittent nature of wind.

Artificial intelligence (AI) has shown significant promise in addressing these challenges, and recent research has explored various AI-based methods for wind power forecasting and monitoring. These methods include machine learning algorithms, deep learning techniques, fuzzy logic, and evolutionary computing. AI-based approaches can improve wind power forecasting and monitoring accuracy and reliability and provide valuable insights for energy management and decision-making.

This Special Issue aims to showcase the latest research in AI methods for wind power forecasting and monitoring. We invite researchers and practitioners to submit original research articles, reviews, and case studies that address the following topics:

(1) AI-based methods for wind power forecasting,

(2) AI-based methods for wind power monitoring,

(3) Integration of AI with traditional forecasting and monitoring methods,

(4) Machine learning algorithms for anomaly detection/identification in wind turbines,

(5) Applications of AI in wind power management and decision-making,

(6) AI-based control scheme for wind turbines,

(7) Challenges and future directions of AI in wind power forecasting and monitoring.

This Special Issue will provide a platform for researchers to share their findings and insights on AI methods for wind power control, forecasting and monitoring. It will also facilitate collaboration and knowledge-sharing between researchers and practitioners in the field, and contribute to developing more accurate and reliable wind power forecasting and monitoring methods.

Dr. Fouzi Harrou
Dr. Ying Sun
Dr. Muddu Madakyaru
Dr. Ramakrishna Kini
Guest Editors

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.