Special Issue "Solar and Wind Energy Forecasting"
Deadline for manuscript submissions: 31 October 2018
The journal Energies (ISSN 1996-1073, IF 2.262) is currently running a Special Issue entitled "Solar and Wind Power and Energy Forecasting". Prof. Dr. Sonia Leva and Dr. Emanuele Ogliari (Politecnico di Milano, Milano, Italy) are serving as Guest Editors for this issue. We think you could make an excellent contribution based on your expertise.
The renewable-energy-based generation of electricity is currently experiencing rapid growth in electric grids. The intermittent input from the renewable energy sources (RES) as a consequence brings problems in balancing the energy supply and demand.
Thus, forecasting of RES power generation is vital to help the grid operators to better manage the electric balance between power demand and supply, and to improve the penetration of distributed renewable energy sources and, in stand-alone hybrid systems, for the optimum size of all its components and to improve the reliability of the isolated systems.
This Special Issue of Energies, “Solar and Wind Power and Energy Forecasting”, is intended for disseminating new promising methods and techniques to forecast the output power and energy of intermittent renewable energy sources.
Prof. Dr. Sonia Leva
Dr. Emanuele Ogliari
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 papers will be 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 monthly 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 1600 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.
- RES integration
- Forecasting techniques
- Machine Learning
- Computational Intelligence
- PV system
- Wind system
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Designing, developing and implementing a forecasting method for the produced and consumed electricity in the case of small wind farms situated on wind deflection hilly terrain
Alexandru Pîrjan, George Căruțașu, Dana-Mihaela PetroșanuAbstract: The accurate forecasting of the produced and consumed electricity from wind farms is an essential aspect for wind power plant operators. In this context, our research addresses small-scale wind farms situated on hilly terrain, having as a main purpose the overcoming of limitations that consist in lowering the forecasting accuracy, arising from the wind deflection, caused by the hilly terrain. A specific aspect of our devised forecasting method consists in incorporating advantages of recurrent long short-term memory (LSTM) neural networks, benefiting from their long-term dependencies learning capabilities and the advantages of feed-forward function fitting neural networks that have the ability to map between a data set of numeric inputs and a set of numeric targets. Another specific element of our approach consists in improving the forecasting accuracy by means of refining the accuracy of the weather data input parameters within the same weather forecast resolution area. The developed method has as main beneficiaries the power plant operators, but it can also be successfully applied in order to assess the energy potential of a wind deflection hilly area, being useful for potential investors who want to build this type of wind farms. The method can be compiled and incorporated in the development of a wide range of customized applications targeting electricity forecasting for small wind farms situated on wind deflection hilly terrain.
Keywords: forecasting method; produced and consumed electricity; small wind farms; hilly terrain; wind deflection; long short-term memory neural networks; feed-forward function fitting neural networks