Search Results (99)

Accurate substation-level Short-Term Load Forecasting (STLF) is essential for secure day-ahead power-system operation, yet localized demand is often affected by meteorological variation and discrete calendar shifts such as statutory holidays and makeup workdays. At this spatial scale, end-to-end forecasting models may over-smooth abrupt local changes and fail to represent peaks and valleys accurately. To address this issue, this study proposes a Calendar-Aware Frequency-Decoupled Framework (CA-FDF) for 24 h ahead substation load forecasting. The load series is decomposed by the Discrete Wavelet Transform (DWT), and the low-frequency component is tracked by a regime-aware Recursive Least Squares (RLS) filter. The residuals are then refined through explicit calendar-state encoding and day-ahead weather forecasts. A Multi-Layer Perceptron (MLP) learns latent weather representations, while SHapley Additive exPlanations (SHAP) interpret calendar- and weather-related effects. Experiments on hourly operational data from 29 anonymized substations in East China show that CA-FDF achieves a Mean Absolute Percentage Error (MAPE) of 1.92% and outperforms representative baselines under the same day-ahead setting. The results indicate that frequency-decoupled residual refinement improves localized load forecasting, with calendar-aware correction contributing the largest gain. Full article

Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems, particularly with the increasing integration of renewable energy and the transition toward smart grids. However, most existing approaches rely on a single forecasting model, despite evidence that model performance varies across datasets and forecasting horizons. To address this limitation, this paper proposes an explainable meta-learning framework for adaptive model selection in STLF. Unlike conventional methods that aim to identify a universally optimal model, the proposed approach learns to select the most suitable model based on dataset characteristics and forecasting conditions. The framework integrates cross-dataset evaluation, meta-feature extraction, and a Random Forest-based meta-learner to dynamically determine the best-performing model. The proposed approach is evaluated on three benchmark power systems—Panama, PJM, and Spanish datasets—under both single-step and multi-horizon forecasting settings. The results provide initial evidence of adaptability across multiple datasets. Specifically, LSTM achieves the best single-step performance on the Panama (MAPE = 2.88%) and PJM (MAPE = 7.71%) datasets, while XGBoost outperforms other models on the Spanish dataset (MAPE = 1.07%). Statistical analysis suggests meaningful performance differences, although these findings should be interpreted with caution due to the limited sample size. Furthermore, SHapley Additive exPlanations (SHAP) are employed to enhance interpretability, revealing that forecasting horizon, data variability, and dataset characteristics are the most influential factors in model selection. Overall, the proposed framework improves forecasting accuracy, robustness, and transparency, while promoting a shift from model-centric design to adaptive, data-driven model selection. The framework offers a structured and explainable approach with potential for practical deployment in smart grid applications. Full article

Short-term load forecasting plays a pivotal role in modern power system operations yet it remains challenging due to the complex spatiotemporal dependencies in load data. This paper proposes a dual-head attention residual network (DARNet) that significantly advances STLF through three key innovations: (1) a hybrid encoder combining 1D-CNN and GRU architectures to simultaneously capture the local load patterns and long-term temporal dependencies, achieving a 28% better locality awareness than that of conventional approaches; (2) a novel dual-head attention mechanism that dynamically models both the inter-temporal relationships and cross-variable dependencies, reducing the feature engineering requirements; and (3) an autocorrelation-adjusted recursive forecasting framework that cuts the multi-step prediction error accumulation by 33% compared to that with standard seq2seq models. Extensive experiments on real-world datasets from three Chinese cities demonstrate DARNet’s superior performance, outperforming six state-of-the-art benchmarks by 21–35% across all of the evaluation metrics (MAPE, SMAPE, MAE, and RRSE) while maintaining robust generalization across different geographical regions and prediction horizons. Full article

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate decomposition with an advanced neural network. First, to address the critical issue that MVMD performance is highly sensitive to its parameter settings, which impacts decomposition quality, a multi-strategy Improved Fruit Fly Optimization Algorithm (IFOA) is developed to task-oriented adaptively tune the key parameters of MVMD, forming an Improved MVMD (IMVMD). This optimization aims to ensure decomposition stability and maximize the relevance for the subsequent forecasting task. Second, to fully leverage the characteristics of the frequency-aligned, multi-channel sub-sequences generated by IMVMD, an Extended LSTM (XLSTM) network is designed. Its serially arranged BisLSTM and mLSTM units are specifically tailored to capture the bidirectional long-term dependencies within each stable sub-sequence and the complex high-dimensional interactions across the aligned sub-sequences, respectively. Evaluated on 15 min resolution data from the Austrian grid, the proposed IMVMD-XLSTM framework achieves a day-ahead forecasting Mean Absolute Percentage Error (MAPE) of 2.45% (±1.41%). This study provides a verifiable and effective solution that couples data-adaptive signal processing with a purpose-built neural architecture to enhance forecasting reliability in renewable-rich power systems. Full article

Short-term load forecasting (STLF) is crucial for ensuring power grid stability and economic dispatch. Its accuracy heavily depends on the quality of the input data. However, collecting operational data via the power system’s communication network poses a significant vulnerability to cyberattacks, particularly stealthy False Data Injection (FDI) attacks. By closely mimicking normal load fluctuations, these attacks evade conventional detection, thus, compromising forecasting reliability. To address this challenge, this paper proposes a novel resilient load forecasting framework that integrates two-stage attack detection with robust ensemble learning. In the detection stage, attack identification is performed through seasonal decomposition and AE-BiLSTM reconstruction, followed by restoration using periodic-consistent historical means and secondary screening via second-order differencing (SOD). In the forecasting stage, an improved Multi-Objective Whale Migration Algorithm (MO-WMA) is employed to adaptively optimize ensemble weights for intelligent fusion, significantly enhancing prediction accuracy and robustness, and providing a generalizable solution for intelligent grid load forecasting. Experiments were conducted on the Independent System Operator of New England (ISO New England, 2012–2014) load dataset under four typical FDI attack scenarios, with test sets including diverse attack intensities and temporal patterns. Results show that the framework achieves $98.98\%$ attack detection accuracy and improves the $R^2$ forecasting metric from 0.9053 to 0.9851, approaching attack-free performance, demonstrating effective recovery of forecasting accuracy and generalization capability. Full article

Electricity load forecasting is of high importance for electricity management. Modern power systems are complex and diverse, resulting in increased randomness and nonlinear factors of electricity load data, which greatly increases the difficulty of forecasting. This paper proposes a hybrid-deep-learning-based load forecasting method, named DCFformer (DFT-CNN-FEDformer), for short-term load forecasting (STLF) tasks. The method first employs the discrete Fourier transform (DFT) to denoise time-sequence data on electricity load, so that fluctuations caused by incidents can be reduced. Secondly, it utilizes a convolutional neural network (CNN) that produces sequences of local features extracted from the denoised time sequences. Thirdly, a FEDformer network is applied to perform load forecasting by using extracted feature sequences. In the experiments, we utilize datasets from three regional power systems or apparatuses to compare the proposed DCFformer with other approaches, and the results show that, under the same conditions, DCFformer outperforms the competitors in forecasting precision, which proves the significance of its performance and practicality. Full article

Short-term load forecasting (STLF) underpins the efficient and secure operation of power systems. This study develops and evaluates a hybrid architecture that couples deep neural networks (DNNs) with support vector regression (SVR) for national-scale day-ahead STLF using Croatian load data from 2006 to 2022. The approach employs an hour-specific framework of 24 hybrid models: each DNN learns a compact nonlinear representation for a given hour, while an SVR trained on the penultimate layer activations performs the final regression. Gradient-boosting-based feature selection yields compact, informative inputs shared across all model variants. To overcome limitations of historical local measurements, the framework integrates global numerical weather prediction data from the TIGGE archive with load and local meteorological observations in an operationally realistic setup. In the held-out test year 2022, the proposed hybrid consistently reduced forecasting error relative to standalone DNN-, LSTM- and Transformer-based baselines, while preserving a reproducible pipeline. Beyond using SVR as an alternative output layer, the contributions are as follows: addressing a 17-year STLF task, proposing an hour-specific hybrid DNN–SVR framework, providing a systematic comparison with deep learning baselines under a unified protocol, and integrating global weather forecasts into a practical day-ahead STLF solution for a real power system. Full article

Accurate Short-Term Load Forecasting (STLF) is paramount for the stable and economical operation of power systems, particularly in the context of high renewable energy penetration, which exacerbates load volatility and non-stationarity. The prevailing advanced “decomposition–ensemble” paradigm, however, faces two significant challenges when processing non-stationary signals: (1) The performance of Variational Mode Decomposition (VMD) is highly dependent on its hyperparameters (K, α), and traditional meta-heuristic algorithms (e.g., GA, GWO, PSO) are prone to converging to local optima during the optimization process; (2) Deep learning predictors struggle to dynamically weigh the importance of multi-dimensional, heterogeneous features (such as the decomposed Intrinsic Mode Functions (IMFs) and external climatic factors). To address these issues, this paper proposes a novel, adaptive hybrid forecasting framework, namely IRIME-VMD-CDA-LSTNet. Firstly, an Improved Rime Optimization Algorithm (IRIME) integrated with a Gaussian Mutation strategy is proposed. This algorithm adaptively optimizes the VMD hyperparameters by targeting the minimization of average sample entropy, enabling it to effectively escape local optima. Secondly, the optimally decomposed IMFs are combined with climatic features to construct a multi-dimensional information matrix. Finally, this matrix is fed into an innovative Cross-Dimensional Attention (CDA) LSTNet model, which dynamically allocates weights to each feature dimension. Ablation experiments conducted on a real-world dataset from a distribution substation demonstrate that, compared to GA-VMD, GWO-VMD, and PSO-VMD, the proposed IRIME-VMD method achieves a reduction in Root Mean Square Error (RMSE) of up to 18.9%. More importantly, the proposed model effectively mitigates the “prediction lag” phenomenon commonly observed in baseline models, especially during peak load periods. This framework provides a robust and high-accuracy solution for non-stationary load forecasting, holding significant practical value for the operation of modern power systems. Full article

Under the ongoing electricity market reforms, short-term load forecasting (STLF) is increasingly challenged by pronounced non-stationarity driven by price fluctuations. This study proposes an adaptive STLF framework tailored to price-induced non-stationarity. Firstly, a market state identification method based on load–price joint clustering is developed to structurally model the temporal interactions between price and load. It allows the automatic extraction of typical market patterns and helps uncover how price fluctuations drive load variations. Secondly, a gated mixture forecasting network is proposed to dynamically adapt to the inertia of historical price fluctuations. By integrating parallel branches with an adaptive weighting mechanism, the model dynamically captures historical price features and achieves both rapid response and steady correction under market volatility. Finally, a Transformer-based expert model with multi-scale dependency learning is introduced to capture sequential dependencies and state transitions across different load regimes through self-attention, thereby enhancing model generalization and stability. Case studies using real market data confirm that the proposed approach delivers substantial performance improvements, offering reliable support for system dispatch and market operations. Relative to mainstream baseline models, it reduces MAPE by 1.08–2.62 percentage points. Full article

Short-term load forecasting (STLF) is a core technical support for ensuring the safe and economic operation of power systems and efficient trading in electricity spot markets. To address the limitations of traditional forecasting models in handling the strong nonlinear and non-stationary characteristics of load data under electricity spot market conditions—where load is influenced by the coupling of multiple factors, such as meteorological conditions, electricity price signals, and seasonal patterns—we propose a hybrid forecasting model (VMD-PSO-LSTM-RF) that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), Random Forest (RF), and Particle Swarm Optimization (PSO) to enhance the forecasting accuracy and market adaptability. First, VMD is applied to adaptively decompose the half-hourly power load data of a comprehensive user in Ningbo, Zhejiang Province, from July 2024 to June 2025. The original load series was decomposed into three components, effectively avoiding the mode aliasing problem common in traditional decomposition methods and providing high-quality inputs for subsequent forecasting. Simultaneously, meteorological data and temporal features were incorporated to construct a multi-dimensional input feature set, meeting the requirements of electricity spot markets for considering multiple influencing factors. Second, the PSO algorithm was used to optimize the key hyperparameters of LSTM and RF with seasonal differentiation. With the optimization, we aimed to maximize the Coefficient of Determination (R²) on the validation set, ensuring that the model parameters precisely matched the load fluctuation characteristics of each season. Finally, based on the feature differences of various frequency components, LSTM and RF were used to construct sub-models, and the final load value was obtained through weighted integration of the prediction results of each component. The results fully demonstrate that the proposed model can accurately capture the multi-scale fluctuation characteristics of load in electricity spot market environments, with forecasting performance superior to traditional single models and basic hybrid models; furthermore, the proposed model achieves precise extraction of multi-scale load features and in-depth temporal pattern mining, providing reliable technical support for efficient electricity spot market operation, as well as empirical references for formulating scenario-specific forecasting strategies and managing trading risks in electricity markets. Full article

Accurate short-term load forecasting (STLF) is essential for energy management in buildings, yet remains challenging due to the nonlinear interactions among weather, occupancy, and operational patterns. This study presents a reproducible forecasting pipeline applied as a case study to a single anonymized hotel in Greece, representing a highly variable building-scale load. Three heterogeneous data streams were programmatically ingested and aligned: distribution-operator smart meter telemetry (15 min intervals aggregated to daily active energy), enterprise guest-night counts as an occupancy proxy, and meteorological observations from the National Observatory of Athens (NOA). Following rigorous preprocessing, feature construction incorporated lagged demand, calendar encodings, and exogenous drivers. Forecasting was performed with a stacked LSTM architecture (BiLSTM → LSTM → LSTM with dropout and a compact dense head), trained and validated under a leakage-safe chronological split. A bounded random hyperparameter search of forty configurations was tracked in MLflow 3.5.0 to ensure full reproducibility. The best model achieved RMSE of 4.71 kWh, MAE of 3.48 kWh, and MAPE of 3.29% on the hold-out test set, with stable training and robust diagnostics. The findings confirm that compact recurrent networks can deliver accurate and transparent hotel-level forecasts, providing a practical template for operational energy planning and sustainability reporting. Future research should extend this case study to multi-building portfolios and hybrid deep learning architectures. Full article

Conventional power load forecasting frameworks face limitations in dynamic spatial topology capture and long-term dependency modeling. To address these issues, this study proposes a hybrid GAT-CNN-LSTM architecture for enhanced short-term power load forecasting. The model integrates three core components synergistically: Graph Attention Network (GAT) dynamically captures spatial correlations via adaptive node weighting, resolving static topology constraints; a CNN-LSTM module extracts multi-scale temporal features—convolutional kernels decompose load fluctuations, while bidirectional LSTM layers model long-term trends; and a gated fusion mechanism adaptively weights and fuses spatio-temporal features, suppressing noise and enhancing sensitivity to critical load periods. Experimental validations on multi-city datasets show significant improvements: the model outperforms baseline models by a notable margin in error reduction, exhibits stronger robustness under extreme weather, and maintains superior stability in multi-step forecasting. This study concludes that the hybrid model balances spatial topological analysis and temporal trend modeling, providing higher accuracy and adaptability for STLF in complex power grid environments. Full article

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. Feature selection incorporated temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for short-term load forecasting (STLF) and very-short-term load forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, with our novel forecasting approaches reducing the Mean Absolute Percentage Error (MAPE) by up to 1–3% for industrial and 1–10% for commercial consumers compared to baseline models, thereby supporting grid stability. Full article

Forecasting electricity consumption is one of the most important scientific and practical tasks in the field of electric power engineering. The forecast accuracy directly impacts the operational efficiency of the entire power system and the performance of electricity markets. This paper proposes algorithms for source data preprocessing and tuning XGBoost models to obtain the most accurate forecast profiles. The initial data included hourly electricity consumption volumes and meteorological conditions in the power system of the Republic of Tatarstan for the period from 2013 to 2025. The novelty of the study lies in defining and justifying the optimal model training period and developing a new evaluation metric for assessing model efficiency—financial losses in Balancing Energy Market operations. It was shown that the optimal depth of the training dataset is 10 years. It was also demonstrated that the use of traditional metrics (MAE, MAPE, MSE, etc.) as loss functions during training does not always yield the most effective model for market conditions. The MAPE, MAE, and financial loss values for the most accurate model, evaluated on validation data from the first 5 months of 2025, were 1.411%, 38.487 MWh, and 16,726,062 RUR, respectively. Meanwhile, the metrics for the most commercially effective model were 1.464%, 39.912 MWh, and 15,961,596 RUR, respectively. Full article

Accurate Short-Term Load Forecasting (STLF) is a critical task in managing and operating smart grids. Existing STLF methods primarily rely on mathematical modeling or neural networks, often struggle to effectively capture the correlations between influencing factors and load data, and frequently lack interpretability. To address these challenges, this paper proposes an intelligent framework for STLF that combines a pattern extraction and attention mechanism, which leverages the characteristics of electricity consumption data. The proposed framework facilitates the integration of prior knowledge, identifies intrinsic data patterns, and more accurately maps the relationships between influencing factors and load patterns. Finally, we conduct experiments on real-world and publicly available datasets to evaluate the performance of the proposed model. Specifically, the proposed model improves the accuracy of STLF compared to that of existing methods and reduces the mean absolute percentage error by $2\%$ to $5\%$. The model performs superiorly on the real datasets, with root mean squared error and mean absolute percentage error values of $0.810$ MWh and $7.09\%$. Full article