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Short-Term Load Forecasting—2nd Edition
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Department of Electrical Engineering, Universidad Politecnica de Cartagena, Cartagena, Spain
Department of Applied Mathematics and Statistics, Universidad Politecnica de Cartagena, Cartagena, Spain
Department of Electrical Engineering, Universidad de La Rioja, La Rioja, Spain
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Short-Term Load Forecasting—2nd Edition

Abstract submission deadline
31 October 2025
Manuscript submission deadline
31 December 2025
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Topic Information

Dear Colleagues,

It is well known that short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies for power systems (planning, scheduling, maintenance, and control processes, among others), and this topic has been an important issue for several decades. However, there is still much progress to be made in this field. The deployment of enabling technologies (e.g., smart meters) has made high granular data available for many customer segments and many tasks—for instance, it has made load forecasting tasks feasible at several demand aggregation levels. The first challenge in this area is the improvement of STLF models and their performance at new demand aggregation levels. Moreover, the increasing inclusion of renewable energies (wind and solar power) in the power system and the necessity of including more flexibility through demand response initiatives have introduced greater uncertainties, creating new challenges for STLF in more dynamic power systems in the near future. Many techniques have been proposed for STLF, including traditional statistical models (such as SARIMA, ARMAX, exponential smoothing, linear and non-linear models, etc.) and artificial intelligence techniques (such as fuzzy regression, artificial neural networks, support vector regression, tree-based regression, ensemble methods, stacked methods, etc.). Furthermore, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new uncertainties in the power system has given more importance to probabilistic load forecasting in recent years. This Topic is concerned with both fundamental research on STLF methodologies and practical application research, aiming at exploring the challenges that will be faced by a more distributed power system in the future. All submitted contributions must be based on the rigorous examination of the mentioned approaches and demonstrate a theoretically sound framework; submissions lacking such a scientific approach are discouraged. It is reccomended that existing/presented approaches are validated using real practical applications.

Prof. Dr. Antonio Gabaldón
Prof. Dr. María Carmen Ruiz-Abellón
Prof. Dr. Luis Alfredo Fernández-Jiménez
Topic Editors

Keywords

  • short-term load forecasting and distributed energy resources
  • short-term load forecasting and demand aggregation levels
  • statistical forecasting models (SARIMA, ARMAX, exponential smoothing, linear and non-linear regression, etc.)
  • artificial neural networks (ANNs)
  • fuzzy regression models
  • tree-based regression methods
  • stacked and ensemble methods
  • evolutionary algorithms
  • deep learning architectures
  • support vector regression (SVR)
  • robust load forecasting
  • hierarchical and probabilistic forecasting
  • hybrid and combined models

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms 		2.1 4.5 2008 17.8 Days CHF 1800 Submit
Applied Sciences
applsci 		2.5 5.5 2011 19.8 Days CHF 2400 Submit
Electricity
electricity 		1.8 5.1 2020 26 Days CHF 1200 Submit
Energies
energies 		3.2 7.3 2008 16.2 Days CHF 2600 Submit
Forecasting
forecasting 		3.2 7.1 2019 22.9 Days CHF 1800 Submit

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Published Papers (1 paper)

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39 pages, 5604 KB  
Article
Prediction of 3D Airspace Occupancy Using Machine Learning
by Cristian Lozano Tafur, Jaime Orduy Rodríguez, Pedro Melo Daza, Iván Rodríguez Barón, Danny Stevens Traslaviña and Juan Andrés Bermúdez
Forecasting 2025, 7(4), 56; https://doi.org/10.3390/forecast7040056 - 8 Oct 2025
Abstract
This research introduces a system designed to predict three-dimensional airspace occupancy over Colombia using historical Automatic Dependent Surveillance-Broadcast (ADS-B) data and machine learning techniques. The goal is to support proactive air traffic management by estimating future aircraft positions—specifically their latitude, longitude, and flight [...] Read more.
This research introduces a system designed to predict three-dimensional airspace occupancy over Colombia using historical Automatic Dependent Surveillance-Broadcast (ADS-B) data and machine learning techniques. The goal is to support proactive air traffic management by estimating future aircraft positions—specifically their latitude, longitude, and flight level. To achieve this, four predictive models were developed and tested: K-Nearest Neighbors (KNN), Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Among them, the LSTM model delivered the most accurate results, with a Mean Absolute Error (MAE) of 312.59, a Root Mean Squared Error (RMSE) of 1187.43, and a coefficient of determination (R2) of 0.7523. Compared to the baseline models (KNN, Random Forest, XGBoost), these values represent an improvement of approximately 91% in MAE, 83% in RMSE, and an eighteen-fold increase in R2, demonstrating the substantial advantage of the LSTM approach. These metrics indicate a significant improvement over the other models, particularly in capturing temporal patterns and adjusting to evolving traffic conditions. The strength of the LSTM approach lies in its ability to model sequential data and adapt to dynamic environments—making it especially suitable for supporting future Trajectory-Based Operations (TBO). The results confirm that predicting airspace occupancy in three dimensions using historical data are not only possible but can yield reliable and actionable insights. Looking ahead, the integration of hybrid neural network architectures and their deployment in real-time systems offer promising directions to enhance both accuracy and operational value. Full article
(This article belongs to the Topic Short-Term Load Forecasting—2nd Edition)
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