Load Prediction Research Articles

Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy

With the gradual penetration of new energy generation and storage to the building side, the short-term prediction of building power demand plays an increasingly important role in peak demand response and energy supply/demand balance. The low occurring frequency of peak electrical loads in buildings leads to insufficient data sampling for model training, which is currently an important factor affecting the performance of short-term electrical load prediction. To address this issue, by using peak data clustering and knowledge transfer from similar buildings, a short-term electrical load forecasting method is proposed. First, a building’s electrical peak loads are clustered through peak/valley data analysis and K-nearest neighbors categorization method, thereby addressing the challenge of data clustering in data-sparse scenarios. Second, for peak/valley data clusters, an instance-based transfer learning (IBTL) strategy is used to transfer similar data from multi-source domains to enhance the target prediction’s accuracy. During the process, a two-stage similar data selection strategy is applied based on Wasserstein distance and locality sensitive hashing. An IBTL strategy, iTrAdaboost-Elman, is designed to construct the predictive model. The performance of proposed method is validated on a public dataset. Results show that the data clustering and transfer learning method reduces the error by 49.22% (MAE) compared to the Elman model. Compared to the same transfer learning model without data clustering, the proposed approach also achieves higher prediction accuracy (1.96% vs. 2.63%, MAPE). The proposed method is also applied to forecast hourly/daily power demands of two real campus buildings in the USA and China, respectively. The effects of data clustering and knowledge transfer are both analyzed and compared in detail.

A novel CALA-STL algorithm for optimizing prediction of building energy heat load

A novel CALA-STL algorithm for optimizing prediction of building energy heat load

Automated data-driven building energy load prediction method based on generative pre-trained transformers (GPT)

Automated data-driven building energy load prediction method based on generative pre-trained transformers (GPT)

A Study on the Energy Absorption Performance of Mine Grooved Conical Tube Energy Absorption Components

When rockbursts occur, hydraulic support is prone to impact failure, which leads to severe casualties and economic losses. To improve the performance of hydraulic support structures under impact loading, a grooved conical tube is designed as an energy absorption device to avoid hydraulic columns being destroyed. The performance of the grooved conical tube during deformation is studied using simulation, considering the wall thickness, cone angle and number of grooves. The equivalent axial load of the grooved conical tube component is derived by studying the energy dissipation path. And the grooved conical tube’s structure is optimized. The results show that the Y3-5-10 (cone angle: 3°; number of grooves: 5; wall thickness: 10 mm) grooved conical tube shows excellent performance among the twenty-seven types of structures. In addition, the equivalent axial load prediction formula for the grooved conical tube has a high prediction accuracy. Furthermore, after multi-objective optimization, the mean square error is decreased by 20.6%, and the effective energy absorption is increased by 6.0%, which is able to make the energy absorption process more stable. Compared with widely used corrugated square tubes, the effective deformation distance of the grooved conical tube is increased by 27.2%, and the effective energy absorption is increased by 37.1%. The grooved conical tube has advantages in its effective deformation distance and effective energy absorption. These results are expected to provide sufficient time for the opening of the support column’s relief valve and to enhance the impact resistance of the hydraulic support, which is highly important for the prevention of rockbursts.

Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer

Given the complexity and dynamic nature of short-term load sequence data, coupled with prevalent errors in traditional forecasting methods, this study introduces a novel approach for short-term load forecasting. The method integrates multi-frequency sequence feature analysis and multi-point correction using the FEDformer model. Initially, variational mode decomposition (VMD) technology decomposes the load sequence into multiple subsequences, each exhibiting distinct frequency characteristics. Subsequently, for each frequency band of the load sequence, the LightGBM algorithm quantifies the correlation between the load and various influencing factors. The filtered features are then input into the FEDformer model, providing preliminary short-term and long-term sequence prediction results. Finally, a point-by-point forecasting method based on a tree model generates multi-point load prediction results by training multiple LightGBM models. Throughout the forecasting process, a weighted threshold α is set, and a hybrid weighting method is utilized to combine the forecast results from different models, culminating in the final short-term load forecast results. Validation of the proposed hybrid model was conducted on an actual dataset from a specific area, The results exhibit higher prediction accuracy, affirming the proposed method as a novel and effective approach for short-term load forecasting.

Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review

Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels.

Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning

The prediction of blast loading for complex structures using deep learning requires extensive training data from field experiments or numerical simulations. However, the destructive nature of explosions complicates the collection of adequate field data, and traditional simulations are often time-consuming. To address these challenges, a Bayesian deep learning approach is proposed that quantifies prediction uncertainty. This method utilizes an uncertain selection strategy to actively choose high-quality samples, enhancing the simulation process and iteratively expanding the training dataset. The experimental results demonstrate that this Bayesian deep active learning method achieves a mean absolute percentage error (MAPE) of 6.1% for peak overpressure predictions. Additionally, more than 73.1% of confidence intervals include true values, with prediction times under 20 ms for single-point blasts. Notably, only 60% of the training data is required to achieve the same accuracy as conventional deep learning methods. This approach facilitates rapid and reliable predictions of blast loading for complex structures while significantly reducing training costs.

Comparison of models for aerothermal load prediction using coupled trajectory simulations of a high lift reentry vehicle

Comparison of models for aerothermal load prediction using coupled trajectory simulations of a high lift reentry vehicle

Contextual Background Estimation for Explainable AI in Temperature Prediction

Accurate weather prediction and electrical load modeling are critical for optimizing energy systems and mitigating environmental impacts. This study explores the integration of the novel Mean Background Method and Background Estimation Method with Explainable Artificial Intelligence (XAI) with the aim to enhance the evaluation and understanding of time-series models in these domains. The electrical load or temperature predictions are regression-based problems. Some XAI methods, such as SHAP, require using the base value of the model as the background to provide an explanation. However, in contextualized situations, the default base value is not always the best choice. The selection of the background can significantly affect the corresponding Shapley values. This paper presents two innovative XAI methods designed to provide robust context-aware explanations for regression and time-series problems, addressing critical gaps in model interpretability. They can be used to improve background selection to make more conscious decisions and improve the understanding of predictions made by models that use time-series data.

Multi-dimensional failure risk load prediction of electric motor based on physical model and data-driven approach

Dynamic prediction of failure risk loads is a critical prerequisite for system damage monitoring and service life evaluation. As the failure mechanism of electric vehicle drive motors under multi-source load excitations is complex, and the risk load poses challenges in rapid acquisition, a physics-based and data-driven method for multi-dimensional failure risk load prediction of motors is proposed. Based on actual operational data collected from users, we analyze the failure risk loads of various components of a motor. A physical simulation model of the motor is established to extract multi-dimensional failure risk load time history, and the effectiveness of the simulation model is verified using data from bench tests. Moreover, using the physical model simulation data as a foundation, a two-stage data-driven model is constructed. In the first stage, the genetic algorithm optimized back propagation neural network (GA-BPNN) model is implemented for multi-dimensional loss prediction of the motor, and serves as the primary input for the second stage model, which integrates prediction accuracy and training efficiency. In the second stage, by comprehensively considering the spatial and temporal correlation features with the input layer reconstructed using correlation matrix weights, an improved convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM) model is employed to predict multi-scale loads. Compared with traditional models, the proposed method exhibits significantly improved prediction accuracy, with R2 greater than 0.95. Furthermore, the damage error between the predicted load and the expected load is within 10%, which provides technical support for reliability evaluation and life prediction of electric motor.

Exercise training load prediction based on improved genetic algorithm

With the advancement of digital information age, all kinds of sports also widely used digital mining technology, the technology can promote athletes’ professional level and physical quality, is in the field of sports improve athletes and coaches more effective way, but the bicycle sports for digital mining and research is still in the early state. Cyclists load ability in training is mainly influenced by the body function, the relationship between the two is complicated, this paper is the training load ability and physical function as a research object, explore the specific connection between the two aspects of cyclists, by constructing the cyclist training load prediction model, to apply in the “bicycle team training analysis system”. Strive to promote the overall development of cycling, to provide a basis for the scientific training of talents and training. The cyclist training load prediction model created in this paper can provide a scientific design basis for the training of athletes. When analyzing the body function, this paper mainly analyzes the influence of athletes on conforming sports through 25 aspects such as maximum oxygen intake, functional threshold power and blood oxygen saturation. Because the factors of athletes and the predicted results show non-linear correlation, this paper selects the BP neural network algorithm in the model algorithm, and the adaptive genetic algorithm of the selection operator is adjusted and optimized to clarify the initial weights and thresholds of BP neural network. Finally, the practice has proved that the improved algorithm has a more prominent global optimization ability, and the accuracy of the model has reached 93.28%.

Short-term Load Forecasting Based on K-medoids Clustering and XGBTide Model

Introduction: Electric power load is significantly influenced by weather conditions, making accurate load prediction under varying weather scenarios essential for effective planning and stable operation of the power system. This paper introduces a short-term load forecasting method that combines k-Medoids clustering and XGB-TiDE. Initially, k-Medoids clusters the original load data into categories such as sunny, high temperature, and rain/snow days. Subsequently, XGBoost identifies critical features within these subsequences. The combined forecast model, XGB-TiDE, is then tailored for each subsequence. Here, the TiDE model's predictions are refined point-by-point using the XGBoost results to derive the final short-term load forecasts. An empirical analysis using real power load data from a specific region demonstrates that our proposed model achieves superior accuracy, especially under extreme weather conditions such as high temperatures and precipitation. Background: Electric power load is significantly influenced by weather conditions, making accurate load prediction under varying weather scenarios essential for effective planning and stable operation of the power system. Objective: Addressing the limitations of short-term load forecasting under extreme weather conditions, this paper introduces a novel approach that leverages k-Medoids clustering and the XGBTiDE model to enhance forecasting accuracy. This method strategically segments power load data into meaningful clusters before applying the robust predictive capabilities of XGB-TiDE, aiming for a significant improvement in forecast precision. Method: Initially, k-Medoids clusters the original load data into categories such as sunny, high temperature, and rain/snow days. Subsequently, XGBoost identifies critical features within these subsequences. The combined forecast model, XGB-TiDE, is then tailored for each subsequence. Here, the TiDE model's predictions are refined point-by-point using the XGBoost results to derive the final short-term load forecasts. Results: The model presented in this study demonstrates outstanding performance across all weather conditions, consistently achieving lower mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to other models. For instance, on a sunny day, this model records an MAE of 8.49, RMSE of 10.29, and MAPE of 2.55%, markedly surpassing the Autoformer, which shows an MAE of 18.29, RMSE of 22.33, and MAPE of 5.50%. These results underscore the superior accuracy of our proposed forecasting approach. Conclusion: An empirical analysis using real power load data from a specific region demonstrates that our proposed model achieves superior accuracy, especially under extreme weather conditions such as high temperatures and precipitation.

Numerical modeling of clamp load relaxation of plastic nuts under varying moisture and fiber contents

Abstract This study presents a numerical approach using a 3D finite element model to quantify the remaining clamp load of a plastic nut joint after a specific time. The viscoelastic relaxation of a thermoplastic nut, which is predominantly screwed on a welding stud, is described by a material card using Prony Series. Prony Series are derived from experimental dynamical mechanical analysis with different moisture and fiber contents of the thermoplastic. Since plastic nuts usually do not have preformed threads, the increased temperatures and resulting stresses from the thread-forming process are considered in the simulation. An FE model is created and verified by substrate stress relaxation tests. Experimental clamp load measurements with miniature compression load cells verify the clamp load prediction and show a good agreement. The developed model is used to analyze the clamp load distribution within the threads and reveals an almost even distribution within the threads.

Dynamic Modeling and Analysis on the Cable Effect of USV/UUV System Under High-Speed Condition

This paper investigates the stability issues of the Unmanned Surface Vehicle (USV)–Unmanned Underwater Vehicle (ROV) system induced by cable loads under real marine conditions and high-speed operation. This study focuses on the dynamic coupling characteristics of cable forces affecting the unmanned platform and outlines the variation patterns of these forces under different operational scenarios. By developing the dynamic models of the USV, UC, and UUV, a comprehensive system model for the unmanned marine platform is constructed. The accuracy of the cable model is validated through experimental results, and the coupling interference effects of the cable during collaborative operations are systematically analyzed from multiple perspectives. Additionally, the cable tension and force behaviors under high-speed cruising conditions are thoroughly examined. The results provide a solid foundation for the development of cable load prediction models for collaborative marine unmanned platforms, and offer both theoretical and numerical insights for dynamic control strategies based on cable force adjustments.

Drivers analysis and future scenario-based predictions of nutrient loads in key lakes and reservoirs of the Yangtze River Catchment.

The excessive nutrient loading in lakes and reservoirs poses significant threats to water quality and ecological health, especially under the influence of global climate change and intensified human activities. This study focuses on the long-term trends in nutrient content and ratios, as well as their driving factors in six major lakes and reservoirs (Chaohu Lake, Danjiangkou Reservoir, Dianchi Lake, Dongtinghu Lake, Poyanghu Lake, and Taihu Lake) within the Yangtze River Catchment from 2002 to 2021. Utilizing Redundancy Analysis, Random Forest and Generalized Additive Model, we identify the shifts in natural and socio-economic factors influencing nutrient concentrations and predict future trends under various scenarios. The main driving factors of nutrient content and their ratios have undergone significant changes at different historical stages, with livestock poultry breeding (LPB) and hydraulic retention time (Res_time) being consistently influential. Our findings highlight the dominant role of livestock and poultry breeding and hydraulic retention time in shaping TN content, whereas TP levels are significantly affected by both natural factors (Temperature and Rainfall) and socio-economic activities. Scenario analyses reveal that despite improvements in water management, nutrient loads remain high, posing ongoing risks of eutrophication. Future lakes nutrient content can meet existing water quality standards under socio-economic development scenarios that significantly reduce hydraulic retention time and the contributions of livestock poultry breeding and other socio-economic drivers.

Optimization analysis of the performance of a ground source heat pump system based on air conditioning load forecasting

Optimization analysis of the performance of a ground source heat pump system based on air conditioning load forecasting

A data model-driven prediction and compensation strategy for load disturbances in CNC machine tool feeding systems via digital twin technology

To address the problem of dynamic performance fluctuations in the feed system of CNC machine tools due to load perturbation, which in turn affects machining accuracy, this paper proposes a load prediction and compensation strategy that combines digital twin technology with the dual-drive mechanism of a data model based on a two-axis feed system. First, a digital twin mechanism model of the CNC machine tool feeding system is constructed to simulate and compensate for the load perturbation and realize the virtual mapping of the actual system behavior. Subsequently, the recursive least squares method with a variable forgetting factor combined with the GRU-gated recurrent neural network method is used to dynamically correct and compensate for the mechanism model’s load prediction and compensation results. Finally, the dynamic updating of the experimental results at the visualization level and the interaction between reality and fiction is realized by building a digital twin experimental platform. The experimental results show that the method can control the relative prediction error within 4% and the relative compensation error within 6%, which can significantly improve the quality of workpiece surface machining and the overall performance of the machine tool.

Time‐Series Load Online Prediction of Wind Turbine Based on Adaptive Multisource Operational Data Fusion

Aiming at the problem that the stochastic change of wind turbine generator (WTG) working conditions and the complex nonlinear relationship between load and operation data make it difficult to predict the short‐term load online, this paper proposes an adaptive multi‐information source data fusion online prediction method for WTG load. Random forest (RF) and WaveNet time series (WTS) are established as subinformation source models, and the influence of input features and historical data on load prediction is considered from horizontal and vertical dimensions. In order to reduce the influence of original data completeness on load prediction, the deviation degree of load prediction of RF and WTS is analyzed. The deviation degree of the subinformation source model is used as the basis for judgment, and it is fused into a multi‐information source load model with adaptive deviation degree analysis to predict the loads on the blades, tower top, and tower bottom of the wind turbine. According to the 15 MW semisubmersible offshore WTG load prediction example, the prediction error of this method is about 4% under normal data conditions and 6% under abnormal data conditions, and the calculation time of 200 sets of test data is 0.053 s, which meets the needs of pitch control and has the potential to be applied in the optimization of pitch control strategy.

Machine learning-based prediction of ultimate load in pultruded glass fibre column under axial compression

ABSTRACT This experimental study investigates the axial compression effect of rectangular pultruded glass fiber-reinforced polymer (P-GFRP) tubular column sections, examining the impact of width-to-thickness ratio (B/t), aspect ratio (H/B), and column height on their structural performance. A total of 27 GFRP columns were subjected to axial compression tests to evaluate their ultimate load and initial stiffness. The columns exhibited a uniform failure pattern, characterized by crushing, mid-section fractures, and longitudinal splitting at the corners. The results revealed a negative correlation between the ultimate load and the aspect ratio, as well as the width-to-thickness ratio. This study utilized advanced machine learning algorithms, namely Response Surface Methodology (RSM) and Artificial Neural Network (ANN), to develop predictive models for the ultimate load of GFRP columns. The RSM model achieved an R2 value of 0.8347, demonstrating good accuracy in predicting ultimate load. The ANN model outperformed the RSM model, with an R criterion exceeding 0.68807 across training, testing, and validation phases, showing a stronger correlation between experimental and predicted outcomes. This research establishes a framework for forecasting the mechanical properties of column sections.

Short-term prediction of trimaran load based on data driven technology

Short-term prediction of trimaran load based on data driven technology