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Forecasting
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Feature Papers of Forecasting 2024
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Feature Papers of Forecasting 2024

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5712

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; 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 2024". 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 2024 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

Published Papers (5 papers)

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Research

26 pages, 21593 KiB  
Article
Forecasting Daily Activity Plans of a Synthetic Population in an Upcoming District
by Rachid Belaroussi and Younes Delhoum
Forecasting 2024, 6(2), 378-403; https://doi.org/10.3390/forecast6020021 - 22 May 2024
Viewed by 357
Abstract
The modeling and simulation of societies requires identifying the spatio-temporal patterns of people’s activities. In urban areas, it is key to effective urban planning; it can be used in real estate projects to predict their future impacts on behavior in surrounding accessible areas. [...] Read more.
The modeling and simulation of societies requires identifying the spatio-temporal patterns of people’s activities. In urban areas, it is key to effective urban planning; it can be used in real estate projects to predict their future impacts on behavior in surrounding accessible areas. The work presented here aims at developing a method for making it possible to model the potential visits of the various equipment and public spaces of a district under construction by mobilizing data from census at the regional level and the layout of shops and activities as defined by the real estate project. This agent-based model takes into account the flow of external visitors, estimated realistically based on the pre-occupancy movements in the surrounding cities. To perform this evaluation, we implemented a multi-agent-based simulation model (MATSim) at the regional scale and at the scale of the future district. In its design, the district is physically open to the outside and will offer services that will be of interest to other residents or users of the surrounding area. To know the effect of this opening on a potential transit of visitors in the district, as well as the places of interest for the inhabitants, it is necessary to predict the flows of micro-trips within the district once it is built. We propose an attraction model to estimate the daily activities and trips of the future residents based on the attractiveness of the facilities and the urbanistic potential of the blocks. This transportation model is articulated in conjunction with the regional model in order to establish the flow of outgoing and incoming visitors. The impacts of the future district on the mobility of its surrounding area is deduced by implementing a simulation in the projection situation. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2024)
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17 pages, 16063 KiB  
Article
Forecasting Convective Storms Trajectory and Intensity by Neural Networks
by Niccolò Borghi, Giorgio Guariso and Matteo Sangiorgio
Forecasting 2024, 6(2), 326-342; https://doi.org/10.3390/forecast6020018 - 19 May 2024
Viewed by 635
Abstract
Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network approach to [...] Read more.
Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network approach to forecast the convective cell trajectory and intensity, using, as an example, a region in northern Italy that is frequently hit by convective storms in spring and summer. The predictor input is constituted by radar-derived information about the center of gravity of the cell, its reflectivity (a proxy for the intensity of the precipitation), and the area affected by the storm. The essential characteristic of the proposed approach is that the neural network directly forecasts the evolution of the convective cell position and of the other features for the following hour at a 5-min temporal resolution without a relevant loss of accuracy in comparison to predictors trained for each specific variable at a particular time step. Besides its accuracy (R2 of the position is about 0.80 one hour in advance), this machine learning approach has clear advantages over the classical numerical weather predictors since it runs at orders of magnitude more rapidly, thus allowing for the implementation of a real-time early-warning system. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2024)
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15 pages, 1038 KiB  
Article
Effective Natural Language Processing Algorithms for Early Alerts of Gout Flares from Chief Complaints
by Lucas Lopes Oliveira, Xiaorui Jiang, Aryalakshmi Nellippillipathil Babu, Poonam Karajagi and Alireza Daneshkhah
Forecasting 2024, 6(1), 224-238; https://doi.org/10.3390/forecast6010013 - 10 Mar 2024
Viewed by 1324
Abstract
Early identification of acute gout is crucial, enabling healthcare professionals to implement targeted interventions for rapid pain relief and preventing disease progression, ensuring improved long-term joint function. In this study, we comprehensively explored the potential early detection of gout flares (GFs) based on [...] Read more.
Early identification of acute gout is crucial, enabling healthcare professionals to implement targeted interventions for rapid pain relief and preventing disease progression, ensuring improved long-term joint function. In this study, we comprehensively explored the potential early detection of gout flares (GFs) based on nurses’ chief complaint notes in the Emergency Department (ED). Addressing the challenge of identifying GFs prospectively during an ED visit, where documentation is typically minimal, our research focused on employing alternative Natural Language Processing (NLP) techniques to enhance detection accuracy. We investigated GF detection algorithms using both sparse representations by traditional NLP methods and dense encodings by medical domain-specific Large Language Models (LLMs), distinguishing between generative and discriminative models. Three methods were used to alleviate the issue of severe data imbalances, including oversampling, class weights, and focal loss. Extensive empirical studies were performed on the Gout Emergency Department Chief Complaint Corpora. Sparse text representations like tf-idf proved to produce strong performances, achieving F1 scores higher than 0.75. The best deep learning models were RoBERTa-large-PM-M3-Voc and BioGPT, which had the best F1 scores for each dataset, with a 0.8 on the 2019 dataset and a 0.85 F1 score on the 2020 dataset, respectively. We concluded that although discriminative LLMs performed better for this classification task when compared to generative LLMs, a combination of using generative models as feature extractors and employing a support vector machine for classification yielded promising results comparable to those obtained with discriminative models. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2024)
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20 pages, 1418 KiB  
Article
Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System
by Devon Barrow, Antonija Mitrovic, Jay Holland, Mohammad Ali and Nikolaos Kourentzes
Forecasting 2024, 6(1), 204-223; https://doi.org/10.3390/forecast6010012 - 7 Mar 2024
Viewed by 1294
Abstract
In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they provide one-on-one online computer-based learning support affording [...] Read more.
In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they provide one-on-one online computer-based learning support affording student modelling, adaptive pedagogical response, and performance tracking. This study provides a detailed description of the design and development of the first Forecasting Intelligent Tutoring System, aptly coined FITS, designed to assist students in developing an understanding of time series forecasting using classical time series decomposition. The system’s impact on learning is assessed through a pilot evaluation study, and its usefulness in understanding how students learn is illustrated through the exploration and statistical analysis of a small sample of student models. Practical reflections on the system’s development are also provided to better understand how such systems can facilitate and improve forecasting performance through training. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2024)
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17 pages, 3633 KiB  
Article
A Composite Tool for Forecasting El Niño: The Case of the 2023–2024 Event
by Costas Varotsos, Nicholas V. Sarlis, Yuri Mazei, Damir Saldaev and Maria Efstathiou
Forecasting 2024, 6(1), 187-203; https://doi.org/10.3390/forecast6010011 - 7 Mar 2024
Cited by 1 | Viewed by 1338
Abstract
Remotely sensed data play a crucial role in monitoring the El Niño/La Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate responses. Since 1980, Earth has witnessed three strong ENSO [...] Read more.
Remotely sensed data play a crucial role in monitoring the El Niño/La Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate responses. Since 1980, Earth has witnessed three strong ENSO events (1982–1983, 1997–1998, 2015–2016). In September 2022, La Niña entered its third year and was unlikely to continue through 2024. Instead, since 2022, forecasts have pointed to a transition from La Niña to a Neutral phase in the summer or late 2023. The onset of El Niño occurred around April 2023, and it is anticipated by sophisticated models to be a strong event through the Northern Hemisphere winter (December 2023–February 2024). The aim of this study is to demonstrate the ability of the combination of two new methods to improve the accuracy of the above claim because El Niño apart from climate anomalies, significantly impacts Earth’s ecosystems and human societies, regulating the spread of diseases by insects (e.g., malaria and dengue fever), and influencing nutrients, phytoplankton biomass, and primary productivity. This is done by exploring first the previous major El Niño events in the period January 1876–July 2023. Our calculations show that the ongoing 2023–2024 El Niño will not be the strongest. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2024)
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