Intelligent Energy Forecasting Solutions: Machine Learning Driving Renewable Energy Advancements
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".
Deadline for manuscript submissions: 10 August 2024 | Viewed by 453
Special Issue Editors
Interests: time series; forecasting; machine learning
Special Issue Information
Dear Colleagues,
Renewable energy plays a vital role in mitigating climate change and achieving a sustainable energy future. Accurate renewable energy forecasting is essential for optimal integration into the grid and maximizing energy utilization. Machine learning (ML) enhances forecasting precision by leveraging historical data, real-time analytics, and advanced algorithms. ML-driven renewable energy forecasting solutions not only ensures grid stability and efficient resource management but also accelerates the transition to clean energy sources, fostering a greener and more resilient planet.
Continuing this imperative momentum, we are excited to announce the call for submissions to the Applied Sciences Special Issue on “Intelligent Energy Forecasting Solutions: Machine Learning Driving Renewable Energy Advancements”. This special issue aims to explore the transformative role of ML in the realm of intelligent energy forecasting for renewable energy advancements. The special issue provides a platform for researchers and practitioners to share insights, methodologies, and practical applications that harness ML algorithms to drive innovation in the renewable energy sector.
The special issue welcomes original research, case studies, and reviews on topics, but are not limited to:
- ML and optimization models for renewable energy forecasting.
- Data analytics and visualization for renewable energy systems.
- Challenges and opportunities for ML in renewable energy.
- Explainable AI for renewable energy forecasting.
We look forward to receiving your contributions to this timely and important topic.
Dr. Waddah Saeed
Prof. Dr. Rozaida Ghazali
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Applied Sciences 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 2400 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.
Keywords
- renewable energy
- forecasting
- machine learning
- optimization
- time series
- big data
- visualization
- data science
- explainable AI
