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New Progress in Electricity Demand Forecasting
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New Progress in Electricity Demand Forecasting

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 10542

Special Issue Editors

Prof. Dr. Diego Carmona-Fernández

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Guest Editor
Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain
Interests: electrical engineering; energy storage systems; integration of distributed generation; modeling of renewable power plants; model validation; solar photovoltaics; wind power
Dr. Andres Honrubia-Escribano

E-Mail Website
Guest Editor
Renewable Energy Research Institute, Escuela Técnica Superior de Ingenieros Industriales de Albacete, Department of Electrical Engineering, Electronics, Control Communications, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: electrical engineering; energy storage systems; integration of distributed generation; modeling of renewable power plants; model validation; solar photovoltaics; wind power
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Manuel Calderón Godoy

E-Mail Website
Guest Editor
Department of Electrical Engineering, Electronics and Automatics, University of Extremadura, 06006 Badajoz, Spain
Interests: hybrid power systems; power engineering computing; SCADA systems; computerised monitoring; control engineering computing; fuzzy control; hydrogen production; photovoltaic power systems; power generation control; power system control; power system measurement; programmable controllers; renewable energy sources; sensors; wind power plants

Special Issue Information

Dear Colleagues,

The world is currently facing a transition from a fossil-fuel-based system to a new scenario in which renewable energies are used in an increasing proportion. This transition will make countries without traditional fuel sources less energy dependent and will also bring energy to users that currently have a more limited access. The change is challenging and the consequences for climate, society and market relations are tremendous, although many issues still have to be overcome to make this process go as smoothly as possible.

One very important task that needs to be achieved to favor a suitable implementation of energy models is to develop and implement trustable forecasts of energy demand all over the world; this is the aim of the present Special Issue.

This Special Issue is related to analyzing, comparing and suggesting energy demand forecasting systems, and within this frame, three questions are to be tackled:

1) The importance of the demand analysis;

2) More trustable forecasting techniques;

3) How to reduce the demand analyzed and forecast in the previous points, through the implementation of actions aimed at improving energy efficiency as well as through the implementation of self-consumption facilities.

Prof. Dr. Diego Carmona-Fernández
Dr. Andres Honrubia-Escribano
Prof. Dr. Manuel Calderón Godoy
Guest Editors

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. Energies 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 2600 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.

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Published Papers (6 papers)

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Research

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17 pages, 3660 KiB  
Article
Ensemble of Artificial Neural Networks for Seasonal Forecasting of Wind Speed in Eastern Canada
by Pia Leminski, Enzo Pinheiro and Taha B. M. J. Ouarda
Energies 2025, 18(11), 2975; https://doi.org/10.3390/en18112975 - 5 Jun 2025
Viewed by 367
Abstract
Efficient utilization of wind energy resources, including advances in weather and seasonal forecasting and climate projections, is imperative for the sustainable progress of wind power generation. Although temperature and precipitation data receive considerable attention in interannual variability and seasonal forecasting studies, there is [...] Read more.
Efficient utilization of wind energy resources, including advances in weather and seasonal forecasting and climate projections, is imperative for the sustainable progress of wind power generation. Although temperature and precipitation data receive considerable attention in interannual variability and seasonal forecasting studies, there is a notable gap in exploring correlations between climate indices and wind speeds. This paper proposes the use of an ensemble of artificial neural networks to forecast wind speeds based on climate oscillation indices and assesses its performance. An initial examination indicates a correlation signal between the climate indices and wind speeds of ERA5 for the selected case study in eastern Canada. Forecasts are made for the season April–May–June (AMJ) and are based on most correlated climate indices of preceding seasons. A pointwise forecast is conducted with a 20-member ensemble, which is verified by leave-on-out cross-validation. The results obtained are analyzed in terms of root mean squared error, bias, and skill score, and they show competitive performance with state-of-the-art numerical wind predictions from SEAS5, outperforming them in several regions. A relatively simple model with a single unit in the hidden layer and a regularization rate of 10−2 provides promising results, especially in areas with a higher number of indices considered. This study adds to global efforts to enable more accurate forecasting by introducing a novel approach. Full article
(This article belongs to the Special Issue New Progress in Electricity Demand Forecasting)
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21 pages, 2925 KiB  
Article
Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management
by Falah Dakheel and Mesut Çevik
Energies 2025, 18(11), 2842; https://doi.org/10.3390/en18112842 - 29 May 2025
Viewed by 441
Abstract
As renewable energy sources and distributed generation become more integrated into modern power systems, accurate short-term electricity load forecasting is increasingly critical for effective smart grid management. Most statistical techniques used in the analysis of time series models, conventional statistical models, often fail [...] Read more.
As renewable energy sources and distributed generation become more integrated into modern power systems, accurate short-term electricity load forecasting is increasingly critical for effective smart grid management. Most statistical techniques used in the analysis of time series models, conventional statistical models, often fail to account for temporal dependencies and inherent non-linear patterns found in real-world energy time series. Methods: To this end, merging the power of both the ML approaches, namely Long Short-Term Memory (LSTM) networks and XGBoost, into hybrid frameworks has become a powerful solution. This work aims to develop a new compound model of LSTM for time series pattern extraction from the temporal data and XGBoost for outstanding predictive performance. To assess the performance of the proposed model, we used the Elia Grid dataset from Belgium, which includes load data recorded every 15 min throughout 2022. Results: When compared to individual models, this hybrid approach outperformed them, achieving a Root Mean Square Error (RMSE) of 106.54 MW, a Mean Absolute Percentage Error (MAPE) of 1.18%, and a coefficient of determination (R2) of 0.994. Discussion: In addition, this study implements an ensemble learning strategy by combining LSTM and XGBoost to improve prediction accuracy and robustness. An experimental attempt to integrate attention mechanisms was also conducted, but it did not enhance the performance and was therefore excluded from the final model. The results extend the literature on the development of fusion-based machine learning models for time series forecasting, and the future work of energy consumption analysis, anomaly detection, and resource allocation in SM grids. Full article
(This article belongs to the Special Issue New Progress in Electricity Demand Forecasting)
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15 pages, 2505 KiB  
Article
Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model
by Shuang Zeng, Chang Liu, Heng Zhang, Baoqun Zhang and Yutong Zhao
Energies 2025, 18(2), 227; https://doi.org/10.3390/en18020227 - 7 Jan 2025
Viewed by 1659
Abstract
To tackle the challenges of limited accuracy and poor generalization in short-term load forecasting under complex nonlinear conditions, this study introduces a Prophet–BO–XGBoost-based forecasting framework. This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the [...] Read more.
To tackle the challenges of limited accuracy and poor generalization in short-term load forecasting under complex nonlinear conditions, this study introduces a Prophet–BO–XGBoost-based forecasting framework. This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the Prophet model for label prediction from a time-series viewpoint. Given that hyperparameters substantially impact XGBoost’s performance, this study leverages Bayesian optimization (BO) to refine these parameters. Using a Gaussian process-based surrogate model and an acquisition function aimed at expected improvement, this framework optimizes hyperparameter settings to enhance model adaptability and precision. Through a regional case study, this method demonstrated improved predictive accuracy and operational efficiency, highlighting its advantages in both runtime and performance. Full article
(This article belongs to the Special Issue New Progress in Electricity Demand Forecasting)
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9 pages, 318 KiB  
Article
Forecasting of Residential Energy Utilisation Based on Regression Machine Learning Schemes
by Thapelo Mosetlhe and Adedayo Ademola Yusuff
Energies 2024, 17(18), 4681; https://doi.org/10.3390/en17184681 - 20 Sep 2024
Viewed by 865
Abstract
Energy utilisation in residential dwellings is stochastic and can worsen the issue of operational planning for energy provisioning. Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest [...] Read more.
Energy utilisation in residential dwellings is stochastic and can worsen the issue of operational planning for energy provisioning. Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest and decision tree) are used to train a forecasting model. The model is based on a yearly dataset and its subset seasonal partitions. The dataset is first preprocessed to remove inconsistencies and outliers. The performance measures of mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to evaluate the accuracy of the model. The results show that the performance of the model can be enhanced with hyperparameter tuning. This is shown with an observed improvement of about 44% in accuracy after tuning the hyperparameters of the decision tree regressor. The results further show that the decision tree model can be more suitable for utilisation in forecasting the partitioned dataset. Full article
(This article belongs to the Special Issue New Progress in Electricity Demand Forecasting)
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Review

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25 pages, 656 KiB  
Review
Electricity Consumption and Efficiency Measures in Public Buildings: A Comprehensive Review
by Aarón Ortiz-Peña, Andrés Honrubia-Escribano and Emilio Gómez-Lázaro
Energies 2025, 18(3), 609; https://doi.org/10.3390/en18030609 - 28 Jan 2025
Cited by 1 | Viewed by 1788
Abstract
Industrialization and the expansion of service sectors have led to a significant increase in electricity consumption. This rising demand has also been observed in public buildings, which account for a considerable share of total electrical energy use. Coupled with the upward trend in [...] Read more.
Industrialization and the expansion of service sectors have led to a significant increase in electricity consumption. This rising demand has also been observed in public buildings, which account for a considerable share of total electrical energy use. Coupled with the upward trend in energy prices, this increase has likewise escalated electricity costs in these sectors. The objective of this review is to compile studies that analyze electricity consumption in large public buildings, with a primary focus on universities, as well as works that propose or implement energy-saving measures aimed at reducing consumption. Throughout this review, it is observed that effective monitoring of consumption as well as the use of demand management systems can reduce electricity consumption by up to 15%. Additionally, the studies collected consistently highlight the need for improvements in real-time data monitoring to enhance energy management. Buildings that implement energy-saving measures achieve reductions in demand exceeding 10%, while those incorporating renewable energy systems are capable of covering between 40% and 50% of their energy needs. Of these systems, solar photovoltaic technology is that most widely adopted by public buildings, primarily due to its adaptability to the architectural characteristics and operational requirements of such facilities. This review underscores the substantial impact that optimized monitoring and renewable energy integration can have on reducing the energy footprint of large public facilities. Full article
(This article belongs to the Special Issue New Progress in Electricity Demand Forecasting)
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29 pages, 3469 KiB  
Review
A Review on Digital Twins and Its Application in the Modeling of Photovoltaic Installations
by Dorotea Dimitrova Angelova, Diego Carmona Fernández, Manuel Calderón Godoy, Juan Antonio Álvarez Moreno and Juan Félix González González
Energies 2024, 17(5), 1227; https://doi.org/10.3390/en17051227 - 4 Mar 2024
Cited by 11 | Viewed by 3912
Abstract
Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the Digital Twin (DT) concept and the interest of its application in photovoltaic installations. It compares how other authors use the DT approach [...] Read more.
Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the Digital Twin (DT) concept and the interest of its application in photovoltaic installations. It compares how other authors use the DT approach in photovoltaic installations to improve the efficiency of the renewable energy generated and consumed, energy prediction and the reduction of the operation and maintenance costs of the photovoltaic installation. It reviews how, by providing real-time data and analysis, DTs enable more informed decision-making in the solar energy sector. The objectives of the review are to study digital twin technology and to analyse its application and implementation in PV systems. Full article
(This article belongs to the Special Issue New Progress in Electricity Demand Forecasting)
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