Search Results (421)

With the large-scale development of smart grids and the integration of renewable energy, the operational complexity and load volatility of power systems have increased significantly, placing higher demands on the accuracy and timeliness of electricity load forecasting. However, existing methods struggle to capture the nonlinear and volatile characteristics of load sequences, often exhibiting insufficient fitting and poor generalization in peak and abrupt change scenarios. To address these challenges, this paper proposes a deep learning model named CGA-LoadNet, which integrates a one-dimensional convolutional neural network (1D-CNN), gated recurrent units (GRUs), and a self-attention mechanism. The model is capable of simultaneously extracting local temporal features and long-term dependencies. To validate its effectiveness, we conducted experiments on a publicly available electricity load dataset. The experimental results demonstrate that CGA-LoadNet significantly outperforms baseline models, achieving the best performance on key metrics with an $R^2$ of 0.993, RMSE of 18.44, MAE of 13.94, and MAPE of 1.72, thereby confirming the effectiveness and practical potential of its architectural design. Overall, CGA-LoadNet more accurately fits actual load curves, particularly in complex regions, such as load peaks and abrupt changes, providing an efficient and robust solution for short-term load forecasting in smart grid scenarios. Full article

Accurate short-term electricity load forecasting is essential for efficient energy management, grid reliability, and cost optimization. This study presents a comprehensive comparison of five supervised learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a hybrid (CNN-LSTM) architecture, and Light Gradient Boosting Machine (LightGBM)—using multivariate data from the Greek electricity market between 2015 and 2024. The dataset incorporates hourly load, temperature, humidity, and holiday indicators. Extensive preprocessing was applied, including K-Nearest Neighbor (KNN) imputation, time-based feature extraction, and normalization. Models were trained using a 70:20:10 train–validation–test split and evaluated with standard performance metrics: MAE, MSE, RMSE, NRMSE, MAPE, and $R^2$. The experimental findings show that LightGBM beat deep learning (DL) models on all evaluation metrics and had the best MAE (69.12 MW), RMSE (101.67 MW), and MAPE (1.20%) and the highest $R^2$ (0.9942) for the test set. It also outperformed models in the literature and operational forecasts conducted in the real world by ENTSO-E. Though LSTM performed well, particularly in long-term dependency capturing, it performed a bit worse in high-variance periods. CNN, GRU, and hybrid models demonstrated moderate results, but they tended to underfit or overfit in some circumstances. These findings highlight the efficacy of LightGBM in structured time-series forecasting tasks, offering a scalable and interpretable alternative to DL models. This study supports its potential for real-world deployment in smart/distribution grid applications and provides valuable insights into the trade-offs between accuracy, complexity, and generalization in load forecasting models. Full article

Accurate very short-term load forecasting (VSTLF) is critical to ensure a secure operation of power systems under increasing uncertainty due to renewables. This study proposes an eXtreme Gradient Boosting (XGBoost)-based VSTLF model that incorporates day-ahead load forecasts (DALF) results and load variation features. DALF results provide trend information for the target time, while load variation, the difference in historical electric load, captures residual patterns. The load reconstitution method is also adapted to mitigate the forecasting uncertainty caused by behind-the-meter (BTM) photovoltaic (PV) generation. Input features for the proposed VSTLF model are selected using Kendall’s tau correlation coefficient and a feature importance score to remove irrelevant variables. A case study with real data from the Korean power system confirms the proposed model’s high forecasting accuracy and robustness. Full article

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 study combines an experimental semi-physical simulation model of an electric propulsion tugboat with four different neural networks to create a real-time simulation model for forecasting total power loads with small samples. The results of repeated experiments demonstrate that the BP neural network effectively forecasts the power load. Subsequently, addressing the limitations of traditional BP neural networks, an optimization approach employing an enhanced particle swarm algorithm and attention mechanism was developed, thereby improving the model’s prediction accuracy and robustness. The experiment shows that the improved prediction model achieves an R² value of 97.42%, demonstrating its effectiveness in forecasting changes in the short-term power load of ships as parameters change. In actual operation, ships can allocate power reasonably and in a timely manner according to the load forecast results, thereby improving the efficiency of the power grid. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

With the transition of the energy structure and the continuous development of smart grids, short-term user-side load forecasting plays a key role in fine power dispatch and efficient system operation. However, existing parameter optimization methods lack multi-dimensional and physically interpretable fitness evaluation. They also fail to fully exploit frequency-domain features of decomposed modal components. These limitations reduce model accuracy and robustness in complex scenarios. To address this issue, this paper proposes a short-term user-side load forecasting method based on the MCPO-VMD-FDFE decomposition-enhanced framework. Firstly, a multi-dimensional fitness function is designed using indicators such as modal energy entropy and energy concentration. The Crested Porcupine Optimizer with Multidimensional Fitness Function (MCPO) algorithm is applied in VMD (Variational Mode Decomposition) to optimize the number of decomposition modes (K) and the penalty factor (α), thereby improving decomposition quality. Secondly, each IMF component obtained from VMD is analyzed by FFT. Key frequency components are selectively enhanced based on adaptive thresholds and weight coefficients to improve feature expression. Finally, a multi-scale convolution module is added to the PatchTST model to enhance its ability to capture local and multi-scale temporal features. The enhanced IMF components are fed into the improved model for prediction, and the final output is obtained by aggregating the results of all components. Experimental results show that the proposed method achieves the best performance on user-side load datasets for weekdays, Saturdays, and Sundays. The RMSE is reduced by 45.65% overall, confirming the effectiveness of the proposed approach in short-term user-side load forecasting tasks. Full article

Accurate and efficient short-term load forecasting is crucial for the secure and stable operation and scheduling of power grids. Addressing the inability of traditional Transformer-based prediction models to capture symmetric correlations between different feature sequences and their susceptibility to multi-scale feature influences, this paper proposes a short-term power distribution network load forecasting model based on an enhanced Komodo Mlipir Algorithm (KMA)—Multivariate Variational Mode Decomposition (MVMD)-Crossformer. Initially, the KMA is enhanced with chaotic mapping and temporal variation inertia weighting, which strengthens the symmetric exploration of the solution space. This enhanced KMA is integrated into the parameter optimization of the MVMD algorithm, facilitating the decomposition of distribution network load sequences into multiple Intrinsic Mode Function (IMF) components with symmetric periodic characteristics across different time scales. Subsequently, the Multi-variable Rapid Maximum Information Coefficient (MVRapidMIC) algorithm is employed to extract features with strong symmetric correlations to the load from weather and date characteristics, reducing redundancy while preserving key symmetric associations. Finally, a power distribution network short-term load forecasting model based on the Crossformer is constructed. Through the symmetric Dimension Segmentation (DSW) embedding layer and the Two-Stage Attention (TSA) mechanism layer with bidirectional symmetric correlation capture, the model effectively captures symmetric dependencies between different feature sequences, leading to the final load prediction outcome. Experimental results on the real power distribution network dataset show that: the Root Mean Square Error (RMSE) of the proposed model is as low as 14.7597 MW, the Mean Absolute Error (MAE) is 13.9728 MW, the Mean Absolute Percentage Error (MAPE) reaches 4.89%, and the coefficient of determination (R²) is as high as 0.9942. Full article

With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article

This work proposes a design technique to facilitate the design and optimization of a highperformance power amplifier (PA) in an automated manner. The proposed optimizationoriented strategy consists of the implementation of four deep neural networks (DNNs), sequentially. Firstly, a bidirectional long short-term memory (BiLSTM)-based DNN is trained based on the X-parameters for which the hyperparameters are optimized through the multi-objective ant lion optimizer (MOALO) algorithm. This step is significant since it conforms to the hidden-layer construction of DNNs that will be trained in the following steps. Afterward, a generative adversarial network (GAN) is employed for forecasting the load–pull contours on the Smith chart, such as gate and drain impedances that are employed for the topology construction of the PA. In the third phase, the classification the BiLSTM-based DNN is trained for the employed high-electron-mobility transistor (HEMT), leading to the selection of the optimal configuration of the PA. Finally, a regression BiLSTMbased DNN is executed, leading to optimizing the PA in terms of power gain, efficiency, and output power by predicting the optimal design parameters. The proposed method is fully automated and leads to generating a valid PA configuration for the determined transistor model with much more precision in comparison with long short-term memory (LSTM)-based networks. To validate the effectiveness of the proposed method, it is employed for designing and optimizing a PA operating from 1.8 GHz up to 2.2 GHz at 40 dBm output power. Full article

To address the impact of distributed energy resource volatility on distribution network fault restoration, this paper proposes a strategy that incorporates net load forecasting. A Bayesian-optimized long short-term memory neural network is used to accurately predict the net load within fault-affected areas, achieving an R² of 0.9569 and an RMSE of 12.15 kW. Based on the forecasting results, a fast restoration optimization model is established, with objectives to maximize critical load recovery, minimize switching operations, and reduce network losses. The model is solved using a genetic algorithm enhanced with quantum particle swarm optimization (GA-QPSO), a hybrid metaheuristic known for its superior global exploration and local refinement capabilities. GA-QPSO has been successfully applied in various power system optimization problems, including service restoration, network reconfiguration, and distributed generation planning, owing to its effectiveness in navigating large, complex solution spaces. Simulation results on the IEEE 33-bus system show that the proposed method reduces network losses by 33.2%, extends the power supply duration from 60 to 120 min, and improves load recovery from 72.7% to 75.8%, demonstrating enhanced accuracy and efficiency of the restoration process. Full article

Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes, this study proposes a Bayesian-optimized GCN-BiLSTM-Adaboost model (abbreviated as GCN-BiLSTM-AB). It combines Graph Convolutional Networks (GCN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and a Bayesian-optimized AdaBoost framework. Firstly, the GCN is employed to capture the spatial correlation features of the input data. Then, the BiLSTM is employed to extract the long-term dependencies of the data time series. Finally, the AdaBoost framework is used to dynamically adjust the base learner weights, and a Bayesian method is employed to optimize the weight adjustment process and prevent overfitting. The experiment results on actual load data from a regional power grid show the GCN-BiLSTM-AB outperforms other compared models in prediction error metrics, with $MAE$, $MAPE$, and $RMSE$ values of 1.86, 3.13%, and 2.26, respectively, which improve the prediction robustness during load change periods. Therefore, the proposed method shows that the synergistic effect of spatiotemporal feature extraction and dynamic weight adjustment improves prediction accuracy and robustness, which provides a new forecasting model with high precision and reliability for power system dispatch decisions. Full article

The accurate short-term forecasting (PV) of power is crucial for grid stability control, energy trading optimization, and renewable energy integration in smart grids. However, PV generation is extremely variable and non-linear due to environmental fluctuations, which challenge the conventional forecasting models. This study proposes a hybrid deep learning architecture, Wavelet Transform–Transformer–Temporal Convolutional Network–Efficient Channel Attention Network–Gated Recurrent Unit (WT–Transformer–TCN–ECANet–GRU), to capture the overall temporal complexity of PV data through integrating signal decomposition, global attention, local convolutional features, and temporal memory. The model begins by employing the Wavelet Transform (WT) to decompose the raw PV time series into multi-frequency components, thereby enhancing feature extraction and denoising. Long-term temporal dependencies are captured in a Transformer encoder, and a Temporal Convolutional Network (TCN) detects local features. Features are then adaptively recalibrated by an Efficient Channel Attention (ECANet) module and passed to a Gated Recurrent Unit (GRU) for sequence modeling. Multiscale learning, attention-driven robust filtering, and efficient encoding of temporality are enabled with the modular pipeline. We validate the model on a real-world, high-resolution dataset of a Moroccan university building comprising 95,885 five-min PV generation records. The model yielded the lowest error metrics among benchmark architectures with an MAE of 209.36, RMSE of 616.53, and an R² of 0.96884, outperforming LSTM, GRU, CNN-LSTM, and other hybrid deep learning models. These results suggest improved predictive accuracy and potential applicability for real-time grid operation integration, supporting applications such as energy dispatching, reserve management, and short-term load balancing. Full article

With the gradual penetration of new energy generation/storage, accurate and reliable load forecasting (LF) plays an increasingly important role in different energy management applications (e.g., power resource allocation, peak demand response, energy supply and demand optimization). In recent years, data-driven and artificial intelligence (AI) technologies have received considerable attention in the field of LF. This study provides a comprehensive review on the existing advanced AI and data-driven techniques used for LF tasks. First, the reviewed studies are classified from the load’s spatial scale and forecasting time scale, and the research gap that this study aims to fill in the existing reviews is revealed. It was found that short-term forecasting dominates in the time scale (accounting for about 83.1%). Second, based on the summary of basic preprocessing methods, some advanced preprocessing methods are presented and analyzed. These advanced methods have greatly increased complexity compared with basic methods, while they can bring significant performance improvements such as adaptability and accuracy. Then, various LF models using the latest AI techniques, including deep learning, reinforcement learning, transfer learning, and ensemble learning, are reviewed and analyzed. These models are also summarized from several aspects, such as computational cost, interpretability, application scenarios, and so on. Finally, from the perspectives of data, techniques, and operations, a detailed discussion is given on some challenges and opportunities for LF. Full article

In urban power planning, it is typically necessary to predict future monthly, quarterly, and annual electricity consumption to conduct advance planning and ensure the stable operation of the power grid. Therefore, accurate medium-term load forecasting is of critical importance for urban power grid planning and operation. However, current research primarily focuses on short-term forecasting, which is largely limited to a single timescale. To address this issue, this paper proposes a combined model for medium-term load forecasting, enabling predictions of loads over multiple timescales within the next year. This can help optimize power supply planning. First, by improving the 3σ criterion and incorporating holiday corrections, the original data are processed. Combining the advantages of the Prophet algorithm in capturing linear relationships and future trends with the Random Forest algorithm in capturing nonlinear relationships, a Prophet–Random Forest combined forecasting model is constructed. This model is then applied to predict the electricity consumption of a city in southern China. The results demonstrate that the proposed model achieves high accuracy in medium-term forecasting and can predict loads across multiple timescales. Specifically, for annual, quarterly, and monthly predictions, the average prediction errors are 1.02%, 2.66%, and 3.92%, respectively, showcasing strong forecasting performance. Full article