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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 5399

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Special Issue Editors

Prof. Dr. Kyung-Bin Song

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Guest Editor

Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea
Interests: load forecasting; load modeling; power system operation; power system economics

Dr. Young-Min Wi

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Guest Editor

Department of Electrical Engineering, Sangmyung University, Seoul 03016, Republic of Korea
Interests: power system operation and planning; particularly in load forecasting and its applications
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Special Issue Information

Dear Colleagues,

The global energy transition, driven by the urgency of the climate crisis, is reshaping the power generation landscape and altering the energy mix. As renewable energy sources expand rapidly, the need for systematic power system planning and stable operation has become more critical than ever. The accurate forecasting of both demand and generation—including intermittent renewable resources—has emerged as a key enabler for effective power system operation and planning.

This Special Issue aims to present recent advances in energy forecasting methodologies and their practical applications. We invite the submission of high-quality original research articles, review papers, and technical papers that address the challenges of forecasting power demand and generation with nonlinear and uncertain characteristics, leveraging both traditional statistical methods and modern machine learning techniques.

Topics of interest include, but are not limited to, the following:

All aspects of energy forecasting’
Very short-term, short-term, medium-term, and long-term load forecasting;
Very short-term, short-term, medium-term, and long-term renewable energy forecasting;
Energy consumption forecasting over various time horizons;
Machine learning-based forecasting approaches;
Artificial intelligence techniques, including support vector machines (SVM), fuzzy inference systems, and artificial neural networks (ANN);
Statistical forecasting models;
Probabilistic and uncertainty-aware forecasting methods;
Forecasting for regional integrated energy systems;
Forecasting that incorporates behind-the-meter (BTM) generation;
Economic impact analysis of forecasting accuracy;
Development and application of advanced forecasting models.

We look forward to your contributions that advance the state of the art in load and energy forecasting for modern power systems.

Prof. Dr. Kyung-Bin Song
Dr. Young-Min Wi
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 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. 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.

Keywords

load forecasting
machine learning
renewable energy
advanced forecasting model

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Published Papers (6 papers)

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Research

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27 pages, 1493 KB

Open AccessArticle

Single-Attention Large Language Model for Efficient Multi-Regional Electricity Demand and Generation Forecasting

by Muhammad Zulfiqar, Kelum A. A. Gamage and M. B. Rasheed

Energies 2026, 19(6), 1522; https://doi.org/10.3390/en19061522 - 19 Mar 2026

Viewed by 553

Abstract

Electricity forecasting is one of the most crucial aspects in maintaining stable, reliable, and autonomous power systems. While recently developed forecasting methods based on large language models can make accurate predictions, these models are still struggling due to their computational complexity, which requires more computing power, and their reliance on carefully designed prompts. This makes them complicated and harder to use in practice. To address this, we propose a Single-Attention Large Language Model (SA-LLM) that uses a unified attention mechanism to understand relationships between main and additional variables, without the need for manually created prompts. The proposed framework has been tested on real electricity supply and demand datasets, which are obtained from major U.S. electricity markets, including PJM, MISO, NYISO, ISO New England, ERCOT, SPP, and CAISO. Experimental results demonstrate that the proposed SA-LLM method outperforms the existing counterpart methods in terms of accuracy and associated errors. More specifically, the SA-LLM has also achieved a 22.5% improvement in the mean absolute error compared with traditional LSTM-based models, while reducing memory usage by 52.1% and training time by 38.4% relative to recent LLM-based methods. Furthermore, the SA-LLM demonstrates strong zero-shot generalization, achieving an additional 18.2% improvement in the MAE on previously unseen regions. Full article

(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)

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13 pages, 1818 KB

Open AccessArticle

Adaptive Multi-Tiered Intraday Load Forecasting Strategy with Real-Time Error Correction

by Aleksandar Selakov, Zoran Janković, Aleksandar Bošković, Slađana Turudić, Zoran Pajić and Srđan Vukmirović

Energies 2026, 19(4), 953; https://doi.org/10.3390/en19040953 - 12 Feb 2026

Viewed by 346

Abstract

This paper presents an adaptive short-term intraday load forecasting strategy designed for the operational requirements of transmission and distribution system operators. Standard forecasting approaches often report strong performance on selected periods, yet real utility operations require accurate predictions for every day and every hour of the year. Deviations during the operating day, caused by unexpected changes in consumer behavior, introduce forecasting errors and financial risk. To address this problem, we propose a multi-tiered forecasting model that selects the base method according to the availability of historically similar days. When many similar days exist, the model uses a pretrained artificial neural network, while linear regression is applied under moderate similarity conditions, and an arithmetic mean is used when only a few similar days are available. A real-time delta correction layer is applied in all cases, using recent intraday measurements to forecast short-term error and adjust the baseline output. This approach enables rapid adaptation to atypical days and intraday anomalies. Testing on five years of utility data demonstrates that the method maintains consistently low MAPE across all days and all hours, providing the level of accuracy needed for intraday market operations and system balancing. Full article

(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)

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20 pages, 2495 KB

Open AccessArticle

Ele-LLM: A Systematic Evaluation and Adaptation of Large Language Models for Very Short-Term Power Load Forecasting

by Yansheng Chen, Miao Chen, Chenchao Hu, Jinxi Wu and Ruilin Qin

Energies 2026, 19(3), 631; https://doi.org/10.3390/en19030631 - 26 Jan 2026

Viewed by 1160

Abstract

Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, Large Language Models (LLMs) have shown considerable potential in processing time-series data, yet their effectiveness in very short-term power load forecasting lacks systematic evaluation. This paper proposes a targeted prompt engineering framework and conducts a systematic empirical study on various LLMs, including GPT-4, Claude-3, Gemini, the Llama series, DeepSeek, and Qwen, comparing them with traditional methods such as ARIMA, BiLSTM, MICN, TimesNet, and VMD-BiLSTM. Furthermore, Ele-LLM, a specialized model based on the Low-Rank Adaptation (LoRA) parameter-efficient fine-tuning strategy, is proposed. Experimental results show that Ele-LLM achieves the best forecasting performance (MAPE = 1.04%), significantly outperforming the best traditional baseline. LLMs also demonstrate notable advantages in few-shot learning, long-sequence dependency modeling, and generalization in complex scenarios. This study provides an evaluation benchmark and practical guidelines for applying LLMs in very short-term power load forecasting, proving their great potential and practical value as an emerging technological pathway. Full article

(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)

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17 pages, 1996 KB

Open AccessArticle

Short-Term Probabilistic Prediction of Photovoltaic Power Based on Bidirectional Long Short-Term Memory with Temporal Convolutional Network

by Weibo Yuan, Jinjin Ding, Li Zhang, Jingyi Ni and Qian Zhang

Energies 2025, 18(20), 5373; https://doi.org/10.3390/en18205373 - 12 Oct 2025

Cited by 1 | Viewed by 870

Abstract

To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, the historical dataset is divided into three weather scenarios (sunny, cloudy, and rainy) to generate training and test samples under the same weather conditions. Second, a TCN is used to extract local temporal features, and BiLSTM captures the bidirectional temporal dependencies between power and meteorological data. To address the non-differentiable issue of traditional interval prediction quantile loss functions, the Huber norm is introduced as an approximate replacement for the original loss function by constructing a differentiable improved Quantile Regression (QR) model to generate confidence intervals. Finally, Kernel Density Estimation (KDE) is integrated to output probability density prediction results. Taking a distributed PV power station in East China as the research object, using data from July to September 2022 (15 min resolution, 4128 samples), comparative verification with TCN-QRLSTM and QRBiLSTM models shows that under a 90% confidence level, the Prediction Interval Coverage Probability (PICP) of the proposed model under sunny/cloudy/rainy weather reaches 0.9901, 0.9553, 0.9674, respectively, which is 0.56–3.85% higher than that of comparative models; the Percentage Interval Normalized Average Width (PINAW) is 0.1432, 0.1364, 0.1246, respectively, which is 1.35–6.49% lower than that of comparative models; the comprehensive interval evaluation index (I) is the smallest; and the Bayesian Information Criterion (BIC) is the lowest under all three weather conditions. The results demonstrate that the model can effectively quantify and mitigate PV power generation uncertainty, verifying its reliability and superiority in short-term PV power probabilistic prediction, and it has practical significance for ensuring the safe and economical operation of power grids with high PV penetration. Full article

(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)

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19 pages, 1040 KB

Open AccessFeature PaperArticle

Very Short-Term Load Forecasting for Large Power Systems with Kalman Filter-Based Pseudo-Trend Information Using LSTM

by Tae-Geun Kim, Bo-Sung Kwon, Sung-Guk Yoon and Kyung-Bin Song

Energies 2025, 18(18), 4890; https://doi.org/10.3390/en18184890 - 15 Sep 2025

Cited by 4 | Viewed by 1685

Abstract

The increasing integration of renewable energy resources, driven by carbon neutrality goals, has intensified load variability, thereby making very short-term load forecasting (VSTLF) more challenging. Accurate VSTLF is essential for the reliable and economical real-time operation of power systems. This study proposes a Long Short-Term Memory (LSTM)-based VSTLF model designed to predict nationwide power system load, including renewable generation over a six-hour horizon with 15 min intervals. The model employs a reconstituted load approach that incorporates photovoltaic (PV) generation effects and computes representative weather variables across the country. Furthermore, the most informative input features are selected through a combination of correlation analyses. To further enhance input sequences, pseudo-trend components are generated using a Kalman filter-based predictor and integrated into the model input. The Kalman filter-based pseudo-trend produced an MAPE of 1.724%, and its inclusion in the proposed model reduced the forecasting error (MAPE) by 0.834 percentage points. Consequently, the final model achieved an MAPE of 0.890%, which is under 1% of the 94,929 MW nationwide peak load. Full article

(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)

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Review

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15 pages, 728 KB

Open AccessReview

AI-Driven Load and Net-Load Forecasting in Renewable-Rich and Electric-Vehicle-Intensive Power Systems: An Evidence-Mapping Review

by Manuel Jaramillo and Diego Carrión

Energies 2026, 19(11), 2571; https://doi.org/10.3390/en19112571 - 26 May 2026

Viewed by 117

Abstract

Load forecasting is no longer only a point-prediction problem for aggregate demand. In renewable-rich and electric-vehicle-intensive power systems, forecasts must support net-load balancing, charging-demand management, uncertainty-aware operation, and spatially coupled decision-making. This review presents a quantitative evidence map based on a curated DOI-linked corpus of 116 papers published between 1960 and 2026. Each paper is coded by dominant model family, application theme, forecast horizon, and frontier feature tags. Publication era and dominant model family are strongly associated (

χ^{2} (21) = 93.69

p = 3.70 \times 10^{- 11}

, Cramérś

V = 0.519

). Post-2020 studies are sharply enriched in transformer/graph-neural-network/foundation-model content (13/43 versus 0/73; Haldane-corrected odds ratio 65.07; Fisher

p = 6.65 \times 10^{- 7}

), electric-vehicle or charging themes (7/43 versus 0/73; odds ratio 30.21;

p = 6.91 \times 10^{- 4}

), and deep-learning content (14/43 versus 7/73; odds ratio 4.36;

p = 2.76 \times 10^{- 3}

). To address category coarseness, the frontier family is further decomposed into transformer-only, graph-neural-network-only, hybrid spatiotemporal, and foundation-model subfamilies. The central conclusion is that the most important forecasting topic for current electrical power systems is not generic short-term load forecasting, but the integrated forecasting stack required by electrified, renewable-rich, and spatially coupled grids. Full article

(This article belongs to the Special Issue Advanced Load Forecasting Technologies for Power Systems)

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