Load Prediction Research Articles

Data-Driven Modeling of Lateral and Cracking Loads in Confined Masonry Walls Using Machine Learning

Confined masonry (CM) is becoming a widely adopted construction building method even in earthquake-prone regions due to its economic viability, construction simplicity, and material availability. However, existing empirical models for predicting lateral and cracking loads often fall short due to varied material properties, detailing of confining elements and construction practices. In this study, machine learning (ML) algorithms, such as Extreme Gradient Boosting (XGB), Random Forest (RF), and Extremely Randomized Tree (ERT), were employed to predict the seismic performance of CM walls, focusing on maximum lateral load capacity and cracking load based on an experimental dataset from 84 published studies, with 59 samples for training and 25 for testing. Different material, load, geometrical, and reinforcement detailing, related to the lateral load capacity of CM, were considered. This study also compares the performance of the existing empirical equations against the proposed ML models. The ML models demonstrated strong predictive capabilities, outperforming empirical equations in both maximum lateral load and cracking load predictions, with XGBoost yielding the highest accuracy, reflected by R2 values of 0.903 for lateral load and 0.876 for cracking load predictions, and lowest the RMSE (28.742 for lateral and 23.982 for cracking load). Additionally, a comparative analysis shows that while some empirical equations produce reasonably accurate predictions, most exhibit significant deviations from experimental results. This study finally employs Partial Dependence Plot (PDP) analysis to explain the importance and contribution of the factors that influence the lateral strength, and concludes that ML models, especially XGBoost, are highly effective in capturing the complex behavior of CM walls under vertical and lateral loads, making them valuable tools for enhancing the accuracy of seismic performance evaluations.

OC6 Phase IV: Validation of CFD Models for Stiesdal TetraSpar Floating Offshore Wind Platform

ABSTRACTWith only a few floating offshore wind turbine (FOWT) farms deployed anywhere in the world, FOWT technology is still in its infancy, building on a modicum of real‐world experience to advance the nascent industry. To support further development, engineers rely heavily on modeling tools to accurately portray the behavior of these complex systems under realistic environmental conditions. This reliance creates a need for verification and validation of such tools to improve reliability of load and dynamic response prediction and analysis capabilities of FOWT systems. The Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project was created under the framework of the International Energy Agency to address this need and considers a three‐sided verification and validation between engineering level models, computational fluid dynamics (CFD), and experimental results. In this paper, a novel floating offshore wind platform, the Stiesdal TetraSpar, is simulated using CFD under the load conditions defined by Phase IV of the OC6 project. The comparison of these CFD results against the experimental results demonstrated the ability to predict the platform response to waves when imposing the measured wave signals as input. Although validation versus experiment was largely successful, the damping behavior was impacted by uncertainties likely originating from the mooring system and sensor umbilical cable. This extensive comparison effort with multiple CFD practitioners offers insight into best practices to achieve reliable results.

Coordinated Dispatch of Power Generation and Spinning Reserve in Power Systems with High Renewable Penetration

The spinning reserve demand in high renewable penetration power systems is increasing significantly due to the stochastic and unpredictable nature of renewable power. This paper defines the expected load not supplied ratio (ELNSR) and models uncertainty factors such as unit forced outage rates, load and wind power output prediction errors based on probability density functions. It derives a quantitative relationship between system operating reserves and the ELNSR and uses this quantitative relationship as a constraint for power generation scheduling. Based on this, a coordinated generation and reserve scheduling model for power systems with large-scale wind power is established. Case study results demonstrate that the proposed model can balance both economy and reliability, coordinate the output allocation between wind power and thermal power, and provide an optimized allocation scheme for operating reserves among thermal power units to meet corresponding reliability requirements.

Fault-tolerant quantum chemical calculations with improved machine-learning models.

Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [J. Comput. Chem. 2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states.

Load forecasting method based on CNN and extended LSTM

With the continuous development of power systems and the growing demand, accurate power load prediction has become essential to ensure stable system operation and optimize energy allocation. The collection and analysis of historical load data have become increasingly important, and the factors affecting load changes have grown more complex. To address this challenge, this paper proposes a short-term load prediction method based on convolutional neural networks (CNN) and multilayer extended long short-term memory (LSTM) neural networks. The method first extracts local features from historical data using 1D convolution operations in the CNN. Then, a multilayer extended LSTM network captures long-term dependencies in the data from multiple dimensions for more accurate load prediction. The prediction experiments use real load and related factor data from a city in eastern Mongolia. Results demonstrate that the proposed method outperforms other deep learning-based load prediction methods in direct single-step, four-step, and 32-step load predictions in terms of MAPE, RMSE, and MAE indexes. Additionally, the proposed model can be extended to more prediction application scenarios, including intelligent transportation and financial analysis.

A recommendation model for optimizing transfer learning hyper-parameter settings in building heat load prediction with limited data samples

The transfer learning method has gained increasing attention in the domain of building load prediction, particularly in scenarios with limited data samples. Its core principle involves leveraging knowledge obtained from abundant data in source buildings to aid the learning process of models for the target buildings. Existing research has predominantly concentrated on optimizing the selection of source building data to improve transfer learning effectiveness, while the optimization of transfer learning hyper-parameter settings is often neglected. This study proposes a recommendation model tailored for transfer learning hyper-parameter settings in the context of small sample prediction for building heat loads. The objective is to automatically suggest suitable transfer learning hyper-parameter combination based on the specific features of the building heat load data samples. In this study, 200 real building profiles were utilized to generate the input–output dataset required for the recommendation model. By employing data mining techniques such as clustering and classification, the correlation between the features of source building data and the most effective transfer learning hyper-parameter combination is investigated. The developed recommendation model for optimal transfer learning hyper-parameter settings achieves a classification accuracy of 90.5%，and the performance evaluation was conducted using an additional dataset of 30 source buildings. The results show that by employing this recommendation model, the prediction error of the target buildings can be reduced by 0.12% to 6.64% compared to the conventional method of empirically determining transfer learning hyper-parameter settings.

Image based ice-field characterization and load prediction in managed ice field

Image based ice-field characterization and load prediction in managed ice field

A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism

Machine learning models, particularly long short-term memory (LSTM) networks, have been extensively employed for heat load prediction in district heating systems (DHS). Nevertheless, the over-reliance on default hyperparameter settings in most methods hinders further enhancement of prediction accuracy. A novel load prediction model is presented, which integrates the whale optimization algorithm (WOA) to refine the hyperparameters of an LSTM model bolstered by an attention mechanism (ATT) and convolutional neural network (CNN). Three hybrid models (WOA-CNN-ATT-LSTM, PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM) are constructed by comparing WOA with particle swarm optimization (PSO) and genetic algorithm (GA). The proposed hybrid models are evaluated against traditional LSTM models using an 1100-hour dataset from a real DHS. The outcomes reveal that the WOA-CNN-ATT-LSTM model surpasses both the PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM models in heat load prediction accuracy, achieving improvements of 1.9% and 3.2% respectively, and attaining the highest prediction accuracy (R2=0.9962, MSE=0.0001, MAE=0.0082). Moreover, the WOA-CNN-ATT-LSTM model demonstrates superior performance across various time scales (half-day, one-day, three-days, and one-week), highlighting its robustness in heat load prediction. This novel model adaptively adjusts its hyperparameters to identify the optimal configuration, thereby significantly augmenting the overall predictive capabilities of the model.

Cognitive Models for Machine Theory of Mind

AbstractSome of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence.

Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis

Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PDF

Similarity of scaled-down tests of water entry slamming considering the effects of atmospheric pressure and density

The water entry cavity and load characteristics obtained through scaled-down tests are correlated with the atmospheric pressure and density at the free surface. The evaluation of the influence of the cavitation number and atmospheric density coefficient is highly essential for scale tests to improve the prototype prediction accuracy. Focusing on the similarity criterion simulations and load prediction of the scaled-down tests, this study conducts the simulation tests of the water entry characteristics of the scaled-down model under different environments: normal pressure, reduced pressure, and reduced pressure and heavy gas replacement. Moreover, the influence of the cavitation number and atmospheric density coefficient on the multiphase flow, slamming load, and air cushion effect is discussed. The “air cushion effect” is formed at the top of the vehicle during water entry process, which affects the peak narrow pulse width slamming load. Furthermore, the “air cushion” experiences expansion–stability–rupture–escape with increasing water invasion depth. As the atmospheric pressure decreases, the gas tends to thin and the retention inertia weakens. The decrease in the “air cushion” buffering capacity leads to the increase in the slamming load and the expansion of the cavity scale formed by the liquid. Excessive simulation of the dynamic pressure results in the delayed closure of the cavity surface and the slow fall of the water curtain. As the atmospheric density increases, the retention inertia of gas increases because of the increase in the molecular mass, the slamming load gradually decreases, and the closure time of the cavitation and water curtain decreases. The research results of this paper provide some reference for the similarity transformation of the scaled-down test and the pre-research of the prototype.

Research on mechanical behavior of corroded RC beams under impact load and peak impact load prediction model

Recent research has extensively investigated the impact behavior of reinforced concrete beams (RC), with growing attention to the degradation caused by corrosion. Despite these efforts, the complex interplay between corrosion rate and impact loading parameters remains insufficiently understood. To address these knowledge gaps, this study examines the effects of corrosion rate and drop hammer height on the dynamic performance of RC beams, which is essential for understanding structural resilience under extreme conditions. Experiments were conducted with corrosion rates of 0 %, 5 %, 10 %, 15 %, and 20 %, and drop hammer heights of 1 m, 2 m, and 3 m. The study provides novel insights into the failure characteristics, dynamic responses, energy dissipation, and load–displacement behavior of corroded RC beams. The results indicated that higher corrosion rates increased both the number and width of cracks. At the same drop hammer height, the peak impact load decreases as the stirrup corrosion level increases. The energy dissipation shows a positive correlation with drop hammer height, with impulse increasing as the height rises. A predictive model for peak impact load was developed, showcasing a strong correlation between experimental and simulation results, offering a new theoretical framework for assessing structural safety under varying degrees of corrosion.

A hybrid predictive model with an error-trigger adjusting method of thermal load in super-high buildings

The precise prediction of thermal load has consistently garnered attention owing to its significant impact on energy conservation in buildings. The methodologies employed primarily concentrate on modeling within steady-state conditions, utilizing time-series data or mechanism model on fixed parameters. However, given the pronounced time-varying and multifaceted disturbance characteristics associated with building loads, current approaches exhibit constrained efficacy in addressing abrupt fluctuations in demand load and managing data noise. This limitation consequently undermines the accuracy of predictions. This paper proposes a novel hybrid model and an error-trigger adjusting strategy for predicting the thermal load in super-high buildings. The model is constructed by combining a thermodynamic model and an error cancellation model. The former, derived from an examination of the variations of material and energy in buildings, is proposed in the form of an approximate resistance-capacitance structure. The latter is developed using a wavelet threshold denoising technique, in conjunction with a convolutional neural network and a long short-term memory network. A self-adaptive state transition algorithm has been proposed, which relies on dynamically adjusting factors within the feasible region to optimize the selection of unknown parameters in the thermodynamic model. To enhance the flexibility of the hybrid model in effectively respond to the intricacies and fluctuations within the thermal conditions of buildings, an error-trigger adaptive updating strategy and a parameter calibration method based on sensitivity analysis are established. The real-world application results demonstrate the effectiveness of the presented hybrid model and the adjusting strategy.

Application of fiber-optic strain sensing technology in high-precision load prediction of aircraft landing gear

In this paper, a compact and lightweight high-precision airborne fiber-optic strain sensor is designed, and a strain-load prediction model based on convolutional neural network and long short-term memory network (ConvLSTM) is proposed to validate the collected landing gear strain data. Firstly, based on the fiber Bragg grating (FBG) sensing principle and simulation validation, small and lightweight strain and temperature sensors were fabricated, and key performance parameters such as sensitivity and linearity were comprehensively investigated, and they were mounted on the left landing gear of the aircraft to conduct strain monitoring by loading a three-way load, and the ConvLSTM model was used to train and test the strain-load mapping with the maximum relative error, average relative error and variance as indicators to evaluate its prediction accuracy and stability. The experimental results show that the strain measurement accuracy is maintained within 2.5 % and the strain sensitivity is as high as 1.3 pm/με within the ± 5000 με measurement range of the strain sensor; the maximum relative errors of the X, Y, and Z load predictions are 6.03 %, 3.75 %, and 4.12 %, respectively, and the overall average relative errors are 2.38 %, 0.27 %, and 0.76 %, with variances of 0.23 N, 0.61 N, and 0.61 N, respectively. 0.23 N, 0.61 N and 0.12 N, indicating that the model predictions are stable and highly accurate, demonstrating higher prediction accuracy when compared with traditional multiple linear regression methods. The results of this research have important application value in the field of aircraft structural health monitoring.

Simulation of dynamic heat dissipation energy optimization based on IoT and image recognition in low carbon building VR design process

With the global emphasis on low-carbon buildings, how to effectively optimize the thermal management of buildings has become a research hotspot. This article constructs a three-dimensional model of low-carbon buildings and implements real-time heat dissipation detection of buildings through image recognition to evaluate their heat dissipation performance. Then a heat load prediction model was proposed, which obtained the heat load prediction results of buildings under different environmental conditions through data analysis. Thus, it lays the foundation for the subsequent optimization of dynamic heat dissipation energy. The core of the research is to establish a dynamic heat dissipation optimization strategy. This article provides a detailed introduction to the principle of dynamic heat dissipation synergy, combined with the thermal network temperature compensation model and dynamic adjustment model, to achieve the expected energy efficiency improvement. Finally, the construction of a dynamic heat dissipation energy optimization control system based on the Internet of Things has strengthened the collection and analysis of real-time temperature data, and its effectiveness and reliability in practical applications have been verified through system testing.

Assessment of impaired reinforced concrete dapped‐end beams: An analytical approach

AbstractReinforced concrete dapped‐end beams (DEBs) represent a unique structural type with the presence of recessed ends. The beam configuration is ideal for structural connections. However, the recessed area is structurally a disturbed region, and is vulnerable to embedded reinforcement deterioration. This study explored the potential of strength assessment of impaired DEBs via an analytical approach. A kinematics based model (KBM) was opted, and it was subjected to significant modifications to account to suit with distinct structural defects that were common to DEBs. The extended model (EKBM) was validated using experimental results available for eight beams. Both the load and failure mode predictions from the EKBM were found to be more accurate than those from the KBM, where the EKBM prediction offset was within a fair range of −23% to 15%. Furthermore, a parametric analysis was carried out to investigate the sensitivity of shear reinforcement deficiency and of reinforcement corrosion in the joint region on the beam load capacity. It revealed that the first two shear links were the most critical links. The load reduction was linearly proportional to the area reduction of reinforcement due to corrosion, however, the trend deviated from that at some combined deficiencies. The corroded reinforcement length was rather insensitive to the resulting beam strength.

Faster than real-time, phase-resolving, data-driven model of wave propagation and wave–structure interaction

A machine learning time-series prediction approach is proposed for wave propagation and wave load prediction. Under unidirectional wave conditions and variable bathymetry, given a wave gauge upstream, a model is shown to reproduce wave elevation or wave forces downstream under irregular steep and either non-breaking or breaking conditions. Attempts to perform the opposite calculation, predicting upstream conditions from downstream measurements, results in higher error, likely due to information loss under breaking conditions. For choice of machine learning approach, comparisons show that the Time-series Dense Encoder (TiDE) approach results in a good balance between model complexity, stability, computational time, and error. Over a flat bottom, time-series of wave elevation can be predicted up to 10 wavelengths away, though with a degraded accuracy compared to shorter distances. Similar results are shown for time-series of forces on a vertical cylinder, showing better results than a simple Morison approach, as used in engineering tools such as OpenFAST, but with a similarly fast computational time. Generalizations show that training on irregular wave data permit extrapolations to periodic wave cases. Finally, the same method also is also demonstrated at field-scale, comparing results between two offshore buoys.

Data-driven optimization of nose profiles for water entry impact load reduction

The prediction and reduction of impact loads during water entry are critical in various engineering applications, such as ship slamming and seaplane landings. Traditional methods, including theoretical models, computational fluid dynamics (CFD), and experimental approaches, often suffer from limited applicability, lengthy processing times, and high costs. To address these challenges, this study explores the use of Deep Neural Networks (DNNs) for predicting peak impact loads during the oblique water entry of cylinders with different nose profiles. Optimized Latin Hypercube Sampling (OLHS) and Sobol sequences are employed to generate dataset inputs from a 7-dimensional parameter space that governs impact dynamics. These inputs are then fed into OpenFOAM solvers to compute the peak impact accelerations, serving as dataset outputs for training and testing the DNN. Experiments are conducted to validate the numerical method, confirming that the resulting dataset outputs can be considered as ground truth. With Bayesian hyperparameter optimization, the well-trained DNN model demonstrates reliable accuracy in predicting peak axial and normal accelerations. Further, this study integrates the DNN model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the nose profiles, resulting in a maximum weighted reduction of 48% in the combined axial and normal accelerations.

RETRACTION: Machine learning based load prediction in smart‐grid under different contract scenario

AbstractRETRACTION: P. K. Yadav, R. Bhasker, A. A. Stonier, G. Peter, A. Vijayakumar, and V. Ganji: Machine learning based load prediction in smart‐grid under different contract scenario. IET Generation, Transmission & Distribution 17, no. 8, 1918‐1931 (2023). https://doi.org/10.1049/gtd2.12828The above article, published online on 27th March 2023 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors‐in‐Chief; Christian Rehtanz and Federico Milano; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.Concerns were raised regarding the article, including the presence of tortured phrases, and duplication of images with the following article:L. Wang, S. Mao, B. M. Wilamowski and R. M. Nelms, “Ensemble Learning for Load Forecasting,” in IEEE Transactions on Green Communications and Networking, 4, no. 2, pp. 616–628 (2020), https://doi.org/10.1109/TGCN.2020.2987304.When the authors were contacted for an explanation, they did not address the concerns adequately. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed and they disagree with the retraction.

Multi-Energy Coupling Load Forecasting in Integrated Energy System with Improved Variational Mode Decomposition-Temporal Convolutional Network-Bidirectional Long Short-Term Memory Model

Accurate load forecasting is crucial to the stable operation of integrated energy systems (IES), which plays a significant role in advancing sustainable development. Addressing the challenge of insufficient prediction accuracy caused by the inherent uncertainty and volatility of load data, this study proposes a multi-energy load forecasting method for IES using an improved VMD-TCN-BiLSTM model. The proposed model consists of optimizing the Variational Mode Decomposition (VMD) parameters through a mathematical model based on minimizing the average permutation entropy (PE). Moreover, load sequences are decomposed into different Intrinsic Mode Functions (IMFs) using VMD, with the optimal number of models determined by the average PE to reduce the non-stationarity of the original sequences. Considering the coupling relationship among electrical, thermal, and cooling loads, the input features of the forecasting model are constructed by combining the IMF set of multi-energy loads with meteorological data and related load information. As a result, a hybrid neural network structure, integrating a Temporal Convolutional Network (TCN) with a Bidirectional Long Short-Term Memory (BiLSTM) network for load prediction is developed. The Sand Cat Swarm Optimization (SCSO) algorithm is employed to obtain the optimal hyper-parameters of the TCN-BiLSTM model. A case analysis is performed using the Arizona State University Tempe campus dataset. The findings demonstrate that the proposed method can outperform six other existing models in terms of Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2), verifying its effectiveness and superiority in load forecasting.