Renewable Energy Forecasting by Computational Intelligence and Big Data

Dear Colleagues,

Computational intelligence (Neural, Fuzzy, and Evolutionary Computation) plays an important role in establishing an interdisciplinary pool of methodologies employed in various industrial fields, in particular, renewable energy. Various forecasting models enabled by computational intelligence have been proposed to facilitate the operation, control, deployment, responses to demand and market trading of renewable energy recently. On the other hand, Internet of Energy (IoE) enables the collection of huge amounts of data during the generation, transmission, distribution and consumption of renewable energy, which calls upon the new solutions to take full advantage of big data while building the forecasting models, in particular, the combination of computational intelligence and big data.

This special issue calls for papers focusing on the solutions to leading edge problems for renewable energy forecasting through bringing big data, innovative modelling and in-depth analysis. We welcome the papers addressing the specific characteristics of renewable energy such as the intermittent pattern in generation, the specific forecasts in various area-based scenarios such as university campus, industrial zone, and the case studies either in the operation of wind farm, or best industrial practice in forecasting system development. In short, this special issue focuses on manuscripts that fill existing research gaps and provide novel ideas for future research in the field, including submissions of all types, such as original research papers, applied case studies, and literature reviews.

Topics of interest for publication include but are not limited to the following:

• New computational framework incorporating big data and computational intelligence
• Big data Analytics facilitating the forecasting models
• Forecasting models of the intermittent generation of renewable energy
• Forecasting models in renewable energy market
• Area-based scenarios of renewable energy forecasting
• Forecasting support systems for renewable energy
• Case studies on renewable energy forecasting
• Other energy forecasting models combing CI and big data

Prof. Dr. Yukun Bao
Dr. Jianzhou Wang
Prof. Dr. Yaoyao He
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. 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.

• renewable energy forecasting
• computational intelligence
• big data
• computational energy modeling
• scenario modeling
• forecasting support system