Feature Papers of Forecasting 2026

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 387

Special Issue Editor


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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; 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
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Special Issue Information

Dear Colleagues,

As the Editor-in-Chief of Forecasting, I am pleased to announce this Special Issue, “Feature Papers of Forecasting 2026”. This Special Issue aims to publish high-quality papers in the field of 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 from 2026 from scholars around the world to be published 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 undergo thorough and rigorous peer-review.

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 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. Forecasting 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 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

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Published Papers (1 paper)

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Research

38 pages, 4155 KB  
Article
From Adoption Diffusion to Dimensioning: Probabilistic Forecasting of 5G/NB-IoT Demand via Monte Carlo Uncertainty Propagation
by Nikolaos Kanellos, Dimitrios Katsianis and Dimitris Varoutas
Forecasting 2026, 8(2), 28; https://doi.org/10.3390/forecast8020028 (registering DOI) - 25 Mar 2026
Viewed by 143
Abstract
Medium-term 5G/NB-IoT planning is made difficult by simultaneous uncertainty in device adoption and per-device traffic behavior because deterministic point forecasts do not quantify overload risk or support reliability-based capacity decisions. A diffusion-to-dimensioning workflow is proposed in which S-curve adoption modeling, bounded usage priors, [...] Read more.
Medium-term 5G/NB-IoT planning is made difficult by simultaneous uncertainty in device adoption and per-device traffic behavior because deterministic point forecasts do not quantify overload risk or support reliability-based capacity decisions. A diffusion-to-dimensioning workflow is proposed in which S-curve adoption modeling, bounded usage priors, scenario stress testing, and Monte Carlo uncertainty propagation are combined to generate predictive demand distributions, exceedance curves, and quantile-based capacity rules. The framework is applied to a Great Britain case study for 2025–2029 using smart meter deployment data and an M2M-based proxy for asset-tracking adoption. Analysis shows that planning-year upper-tail outcomes are driven primarily by asset-tracking usage uncertainty rather than by proxy scale alone. A ±30% perturbation of the AT adoption anchor changes Q0.95 by approximately ±29.8%, whereas stressed AT usage increases Q0.95 by 74.4%. Plausible positive dependence among key AT operational inputs further raises Q0.95 by 18.3–22.5%. Limited hold-out evaluation provides strong out-of-sample support for the smart meter adoption stage and plausibility-only support for the shorter AT proxy. The framework is intended for medium-term, data-lean planning settings and is designed to support transparent risk-based capacity decisions rather than deterministic point sizing. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2026)
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