Short-Term Load Forecasting—2nd Edition

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.1	4.5	2008	19.2 Days	CHF 1800
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400
Electricity electricity	1.8	5.1	2020	26.9 Days	CHF 1200
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600
Forecasting forecasting	3.2	7.1	2019	26.3 Days	CHF 1800

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Published Papers (3 papers)

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All Algorithms Applied Sciences Electricity Energies Forecasting

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22 pages, 2595 KB  

Open AccessArticle

Comprehensive Analysis of Weather and Commodity Impacts on Day-Ahead Electricity Market Using Public API Data with Development of an Accessible Forecasting Mode

by Martin Matejko and Peter Braciník

Electricity 2026, 7(1), 10; https://doi.org/10.3390/electricity7010010 - 2 Feb 2026

Viewed by 232

Abstract

A wide range of factors affect the dynamic and complex environment that is the commodity market. The most significant of these are external drivers, such as political decisions and weather conditions, which cannot be directly controlled. Nevertheless, specific characteristics and price behaviors are [...] Read more.

A wide range of factors affect the dynamic and complex environment that is the commodity market. The most significant of these are external drivers, such as political decisions and weather conditions, which cannot be directly controlled. Nevertheless, specific characteristics and price behaviors are exhibited by individual commodities, which manifest through seasonal patterns and characteristic fluctuations. This study aimed to analyze the day-ahead electricity market and identify the key factors shaping electricity price formation. Particular focus was given to the role of meteorological variables and the interrelationships between the prices of other commodities, such as natural gas, coal, and oil. The analysis combined empirical techniques, such as Fourier transform and correlation analysis, with a predictive LSTM model using a Seq2Seq architecture to forecast short-term electricity prices. A basic forecast of electricity prices in the day-ahead market was provided by a simple predictive model that was developed based on the findings. The results highlight the interconnectedness of energy markets and confirm that external factors play a crucial role in shaping electricity prices. Full article

(This article belongs to the Topic Short-Term Load Forecasting—2nd Edition)

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24 pages, 5750 KB  

Open AccessArticle

A Highly Accurate and Efficient Statistical Framework for Short-Term Load Forecasting: A Case Study for Mexico

by Luis Conde-López, Monica Borunda, Gerardo Ruiz-Chavarría and Tomás Aparicio-Cárdenas

Forecasting 2026, 8(1), 3; https://doi.org/10.3390/forecast8010003 - 5 Jan 2026

Viewed by 514

Abstract

Short-term load forecasting is fundamental for the effective and reliable operation of power systems. Very accurate forecasting methods often involve complex hybrid approaches that combine statistical, physical, and/or intelligent techniques. In this work, we present an innovative, clear, and effective methodology for short-term [...] Read more.

Short-term load forecasting is fundamental for the effective and reliable operation of power systems. Very accurate forecasting methods often involve complex hybrid approaches that combine statistical, physical, and/or intelligent techniques. In this work, we present an innovative, clear, and effective methodology for short-term hourly peak load forecasting that is both simple and highly accurate. The methodology is based on the load forecast used for electricity market purposes, together with fine-tuning dynamic estimation. As a case study, the methodology was applied and tested in Mexico’s interconnected power system. It was implemented across various regions and at both regional and load-\ zone levels of this interconnected power system and, even under a variety of standard and extreme load conditions, achieved outstanding results. Full article

(This article belongs to the Topic Short-Term Load Forecasting—2nd Edition)

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39 pages, 5604 KB  

Open AccessArticle

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

Viewed by 1446

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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