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Forecasting
Special Issues
Feature Papers of Forecasting 2025
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Feature Papers of Forecasting 2025

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 455

Special Issue Editor

Prof. Dr. Sonia Leva

E-Mail Website1 Website2
Guest Editor
Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Interests: photovoltaic system; grid; power sharing; inverters; forecasting; nowcasting; machine learning; degradation; battery management systems; polymer solar cells; organic photovoltaics; electric vehicle; vehicle-to-grid; grid; microgrid; energy systems; maximum power point trackers; electric power plant loads; electricity price; power markets; heterogeneous networks; base stations; energy efficiency, life cycle assessment; wind power; regenerative braking; bicycles; motorcycles; car sharing; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the Editor-in-Chief of Forecasting, I am glad to announce the Special Issue "Feature Papers of Forecasting 2025". This Special Issue is designed to publish high-quality papers in forecasting. We welcome submissions from Editorial Board Members and outstanding scholars invited by the Editorial Board and the Editorial Office. The scope of this Special Issue includes, but is not limited to, the following topics: power and energy forecasting; forecasting in economics and management; forecasting in computer science; weather and forecasting; and environmental forecasting.

We will select 10–20 papers in 2025 from excellent scholars around the world to publish for free for the benefit of both authors and readers.

You are welcome to send short proposals for submissions of feature papers to our Editorial Office (forecasting@mdpi.com). They will first be evaluated by academic editors, and, then, selected papers will be thoroughly and rigorously peer reviewed.

Prof. Dr. Sonia Leva
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 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. Forecasting is an international peer-reviewed open access quarterly 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 1800 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

  • power and energy forecasting
  • forecasting in economics and management
  • forecasting in computer science
  • weather and forecasting
  • environmental forecasting

Benefits of Publishing in a Special Issue

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

27 pages, 3331 KiB  
Article
Wind Speed Forecasting with Differentially Evolved Minimum-Bandwidth Filters and Gated Recurrent Units
by Khathutshelo Steven Sivhugwana and Edmore Ranganai
Forecasting 2025, 7(2), 27; https://doi.org/10.3390/forecast7020027 - 10 Jun 2025
Abstract
Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind prediction accuracy depends [...] Read more.
Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind prediction accuracy depends heavily on the decomposition level (L) and the wavelet filter technique selected. Hence, we examined the efficacy of wind predictions as a function of L and wavelet filters. In the proposed hybrid approach, differential evolution (DE) optimises the decomposition level of various wavelet filters (i.e., least asymmetric (LA), Daubechies (DB), and Morris minimum-bandwidth (MB)) using the maximal overlap discrete wavelet transform (MODWT), allowing for the decomposition of wind data into more statistically sound sub-signals. These sub-signals are used as inputs into the gated recurrent unit (GRU) to accurately capture wind speed. The final predicted values are obtained by reconciling the sub-signal predictions using multiresolution analysis (MRA) to form wavelet-MODWT-GRUs. Using wind data from three Wind Atlas South Africa (WASA) locations, Alexander Bay, Humansdorp, and Jozini, the root mean square error, mean absolute error, coefficient of determination, probability integral transform, pinball loss, and Dawid-Sebastiani showed that the MB-MODWT-GRU at L=3 was best across the three locations. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2025)
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