Search Results (18)

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 increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

This paper introduces a novel weighted fusion methodology for grid-level short-term load forecasting that addresses the critical limitations of direct aggregation methods currently used by regional dispatch centers. Traditional approaches accumulate provincial forecasts without considering regional heterogeneity in load characteristics, data quality, and forecasting capabilities. Our methodology implements a comprehensive evaluation index system that quantifies forecast trustworthiness through three key dimensions: forecast reliability, provincial impact, and forecasting complexity. The core innovation lies in our principal component analysis (PCA)-based weighted aggregation mechanism that dynamically adjusts provincial weights according to their evaluated reliability, further enhancing through time-varying weights that adapt to changing load patterns throughout the day. Experimental validation across three representative seasonal periods (moderate temperature, high temperature, and winter conditions) substantiates that our weighted fusion approach consistently outperforms direct aggregation, achieving a 24.67% improvement in overall MAPE (from 3.09% to 2.33%). Performance gains are particularly significant during critical peak periods, with up to 62.6% error reduction under high-temperature conditions. The methodology verifies remarkable adaptability across different temporal scales, seasonal variations, and regional characteristics, consistently maintaining superior performance from ultra-short-term (1 h) to medium-term (168 h) forecasting horizons. Analysis of provincial weight dynamics reveals intelligent redistribution of weights across seasons, with summer months characterized by Jiangsu dominance (0.30–0.35) shifting to increased Anhui contribution (0.30–0.35) during winter. Our approach provides grid dispatch centers with a computationally efficient solution for enhancing the integration of heterogeneous forecasts from diverse regions, leveraging the complementary strengths of individual provincial systems while supporting safer and more economical power system operations without requiring modifications to existing forecasting infrastructure. Full article

This paper proposes a fault self-healing recovery strategy for passive low-voltage power station areas (LVPSAs). Firstly, being aware of the typical structure and communication conditions of the LVPSAs, a fog computing load forecasting method is proposed based on a dynamic aggregation of incremental learning models. This forecasting method embeds two weighted ultra-short-term load forecasting techniques of complementary characteristics and mines real-time load to learn incrementally, and thanks to this mechanism, the method can efficiently make predictions of low-voltage loads with trivial computational burden and data storage. Secondly, the low-voltage power restoration problem is overall formulated as a three-stage mixed integer program. Specifically, the master problem is essentially a mixed integer linear program, which is mainly intended for determining the reconfiguration of binary switch states, while the slave problem, aiming at minimizing load curtailment constrained by power flow balance along with inevitable load forecast errors, is cast as mixed integer type-1 Wasserstein distributionally robust optimization. The column-and-constraint generation technique is employed to expedite the model-resolving process after the slave problem with integer variables eliminated is equated with the Karush–Kuhn–Tucker conditions. Comparative case studies are conducted to demonstrate the performance of the proposed method. Full article

Extreme scenarios involving abnormal load fluctuations pose serious challenges to the safe and stable operation of power systems. To address these challenges, an ultra-short-term load forecasting model is proposed, specifically designed for extreme conditions. The model combines density-based spatial clustering of applications with noise (DBSCAN), random search Bayesian optimization (RSBO), bidirectional gated recurrent units (BiGRUs), k-nearest neighbor (KNN), and an attention mechanism, enhanced by a fine-tuning strategy to improve forecasting accuracy. Firstly, the original load data are reconstructed weekly, and extreme scenarios are identified using the DBSCAN. Secondly, the RSBO is employed to optimize model parameters within the high-dimensional search space. To further refine performance, the final fully connected layer is fine-tuned to adapt to extreme conditions. Finally, case studies demonstrate that the proposed approach reduces the root mean square error (RMSE) by 12.37% and the mean absolute error (MAE) by 6.73% compared to benchmark models, achieving superior accuracy under all tested extreme scenarios. Full article

Dynamic line rating (DLR) technology dynamically adjusts the current-carrying capacity of transmission lines based on real-time environmental parameters and plays a critical role in maximizing line utilization, alleviating power flow congestion, and enhancing the security and economic efficiency of power systems. However, the strong coupling between the dynamic capacity and environmental conditions increases the system’s sensitivity to multiple uncertainties and causes complications in the overload risk assessment. Furthermore, conventional evaluation methods struggle to meet the minute-level risk refresh requirements in ultrashort-term forecasting scenarios. To address these challenges, in this study, an analytical overload risk assessment framework is proposed based on the second-order reliability method (SORM). By transforming multidimensional probabilistic integrals into analytical computations and establishing a multiscenario stochastic analysis model, the framework comprehensively accounts for uncertainties such as component random failures, wind power fluctuations, and load variations and enables the accurate evaluation of the overload probabilities under complex environmental conditions with DLR implementation. The results from this study provide a robust theoretical foundation for secure power system dispatch and optimization using multiscenario coupled modeling. The effectiveness of the proposed methodology is validated using case studies on a constructed test system. Full article

A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Then, each IMF, along with other influential features, is subjected to data dimensionality reduction and clustering denoising using t-distributed stochastic neighbor embedding (t-SNE) and fast density-based spatial clustering of applications with noise (FDBSCAN) to perform major feature selection. Subsequently, the reduced and denoised data are reconstructed, and a time-aware long short-term memory (T-LSTM) artificial neural network is employed to fill in missing data by incorporating time interval information. Finally, the selected multi-factor load time series is used as input into a support vector regression (SVR) model optimized using the quantum particle swarm optimization (QPSO) algorithm for load prediction. Using measured load data from a specific user-level IES at the Tempe campus of Arizona State University, USA, as a case study, a comparative analysis between the VTDS method and other approaches is conducted. The results demonstrate that the method proposed in this study achieved higher accuracy in short-term forecasting of the IES’s multiple loads. Full article

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting. Full article

With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). In detail, the K-means clustering algorithm was utilized to divide the historical data into different clusters. Through EEMD, the load data of each cluster were decomposed into several sub-sequences with different time scales. The LSTNet (Long- and Short-term Time-series Network) was adopted as the load forecasting model for these sub-sequences. The forecast results for different sub-sequences were combined as the expected result. The proposed method predicts the load in the next 4 h with an interval of 15 min. The experimental results show that the proposed method obtains higher prediction accuracy than other comparable forecasting models. Full article

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is performed via blind tests and the test results are consistent. Full article

With the continuous development of new power systems, the load demand on the user side is becoming more and more diverse and random, which also brings difficulties in the accurate prediction of power load. Although the introduction of deep learning algorithms has improved the prediction accuracy to a certain extent, it also faces problems such as large data requirements and low computing efficiency. An ultra-short-term load forecasting method based on the windowed XGBoost model is proposed, which not only reduces the complexity of the model, but also helps the model to capture the autocorrelation effect of the forecast object. At the same time, the real-time electricity price is introduced into the model to improve its forecast accuracy. By simulating the load data of Singapore’s electricity market, it is proved that the proposed model has fewer errors than other deep learning algorithms, and the introduction of the real-time electricity price helps to improve the prediction accuracy of the model. Furthermore, the broad applicability of the proposed method is verified by a sensitivity analysis on data with different sample sizes. Full article

In order to reduce the influence of abnormal data on load forecasting effects and further improve the training efficiency of forecasting models when adding new samples to historical data set, an ultra-short-term load dynamic forecasting method considering abnormal data reconstruction based on model incremental training is proposed in this paper. Firstly, aiming at the abnormal data in ultra-short-term load forecasting, a load abnormal data processing method based on isolation forests and conditional adversarial generative network (IF-CGAN) is proposed. The isolation forest algorithm is used to accurately eliminate the abnormal data points, and a conditional generative adversarial network (CGAN) is constructed to interpolate the abnormal points. The load-influencing factors are taken as the condition constraints of the CGAN, and the weighted loss function is introduced to improve the reconstruction accuracy of abnormal data. Secondly, aiming at the problem of low model training efficiency caused by the new samples in the historical data set, a model incremental training method based on a bidirectional long short-term memory network (Bi-LSTM) is proposed. The historical data are used to train the Bi-LSTM, and the transfer learning is introduced to process the incremental data set to realize the adaptive and rapid adjustment of the model weight and improve the model training efficiency. Finally, the real power grid load data of a region in eastern China are used for simulation analysis. The calculation results show that the proposed method can reconstruct the abnormal data more accurately and improve the accuracy and efficiency of ultra-short-term load forecasting. Full article

Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM. Full article

Ultra-short-term electrical load forecasting is an important guarantee for the safety and efficiency of energy system operation. Temperature is also an important factor affecting the changes in electric load. However, in different cases, the impact of temperature on load forecasting will vary greatly, and sometimes even lead to the decrease of forecasting accuracy. This often brings great difficulties to researchers’ work. In order to make more scientific use of temperature factor for ultra-short-term electrical load forecasting, especially to avoid the negative influence of temperature on load forecasting, in this paper we propose an ultra-short-term electrical load forecasting method based on temperature factor weight and long short-term memory model. The proposed method evaluates the importance of the current prediction task’s temperature based on the change magnitude of the recent load and the correlation between temperature and load, and therefore the negative impacts of the temperature model can be avoided. The mean absolute percentage error of proposed method is decreased by 1.24%, 1.86%, and 6.21% compared with traditional long short-term memory model, back-propagation neural network, and gray model on average, respectively. The experimental results demonstrate that this method has obvious advantages in prediction accuracy and generalization ability. Full article

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error. Full article