Proposed Prediction Model Research Articles

A predictive framework for last-mile delivery routes considering couriers’ behavior heterogeneity

Last-mile route prediction is a powerful tool for freight delivery companies that can be essential in the development of new features such as arrival time prediction or accurate workload allocation. Existing methodologies propose prediction models that, on the one hand, require external information, such as traffic data or a prior division of work areas, and, on the other hand, assume courier homogeneity in the search for travel patterns. However, this assumption may introduce noise in the predictions since different couriers may be subject to different routing habits. This study proposes a comprehensive predictive framework for delivery routes, which allows identifying the routing profiles that best suit each courier and work area, and which starts from basic information available for any carrier, ensuring the scalability of the tool. The analysis is supported by a case study, where the results obtained bring to light the heterogeneity in the routing decisions of the different drivers and show that the proposed approach produces consistent and accurate predictions for the vast majority of them.

Compressive stress-strain response of freshly compressed rubberized concrete: An experimental and theoretical study

Although using crumb tire rubber concrete (CTRC) in constructing structural elements is accompanied by various environmental and economic advantages, it always comes with challenges due to a significant loss in some mechanical properties of this concrete type. To tackle this challenge, a novel technique of compressing fresh concrete was used, and its impacts on improving the mechanical features of CTRC were investigated. For this purpose, concrete specimens containing crumb tire rubber (CTR), as the fine aggregate substitute, were constructed considering the variables of the water-to-cement ratio (w/c), volume percentage of CTR, and the fresh concrete pressure level as a fraction of steel tube (mold) yielding stress. After the application of a specified short-term pressure to the fresh mix inside the steel molds and the time required for the concrete setting, the steel tubes were removed after curing, and the concrete core alone was exposed to an axial compressive load. According to the test results, the mechanical properties, weight loss resulting from compressing the fresh concrete, energy absorption, failure pattern, the concrete microstructure, water absorption, and porosity were evaluated. By applying an initial axial pressure of 25–80 MPa on the mixes containing 0 %, 15 %, and 30 % CTR replacing the fine aggregate, the compressive strength, the ultimate strain, the toughness, and the initial modulus of elasticity experienced 100–140 % increase, 130–420 % increase, 120–1140 % increase, and 66–70 % decrease, respectively, compared with the uncompressed specimen. Finally, a regression analysis was employed to propose prediction models for the compressive capacity, modulus of elasticity, peak strain, ultimate strain, and relative toughness of freshly compressed concrete containing CTR particles; these models show proper agreement with the experimental results.

Solar Radiation Prediction in PV Power Systems: a Comparison of Deep Learning Models Using Big Data

Photovoltaic (PV) energy systems are one of the most significant renewable resources, requiring efficient solutions for solar power generation and maintenance. Accurately predicting PV energy generation and solar radiation is essential for managing grid maintenance and making energy market decisions. This study proposes prediction models based on the use of deep learning, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, to fill the gap in big data analysis as more renewable energy data is collected. These models will be compared with traditional machine learning methods including Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Data from the GAPYENEV Centre at the University of Harran is used to implement predictive models. Several prediction error metrics such as MAE, MSE, RMSE, R2 and accuracy are used to evaluate the predictive ability of the models.

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

When tunneling, detecting abrupt changes in geological circumstances can be difficult. In recent years, the proliferation of tunneling characteristics has been strongly related to the surrounding geology. These parameters offer significant potential for using data-driven artificial intelligence (AI) approaches to infer patterns from information without reference to known consequences. This research introduces the Simulated Fire Hawk Optimizer-based Deep Action Selection Network (SFHO-DASN) model, which uses a Simulated Fire Hawk Optimizer to anticipate the geological conditions needed for tunneling. The optimized hybrid neural network technique can anticipate geological conditions effectively, as demonstrated by a case study of the constructed model. It is especially significant for rock types, water intrusion, karst caverns, and surface subsidence, for which the predicted accuracy is greater than 95%. These findings imply that the geological circumstances behind the tunnel face could be reliably and correctly predicted by a DASN that has received the proper training. This procedure's most significant advantage is its ability to adjust every scored parameter's weighting in response to variations in geological circumstances. The accuracy performance of the proposed neural network outperforms the conventional neural network, as indicated by the area under the curve (AUC) and performance analysis. A proposed model for geology prediction can attain predictive accuracy with a small number of tunneling parameters, according to an analysis of the feature relevance of each tunneling parameter. The suggested approach ought to be more practical for proposals about tunnel support architecture in the East Asian geological region and for future tunnel building.

Structural Behavior of Circular Concrete Columns Reinforced with Longitudinal GFRP Rebars under Axial Load

This paper presents experimental and theoretical assessments of the structural behavior of circular steel fiber-reinforced concrete (SFRC) columns reinforced with glass fiber-reinforced polymer (GFRP) bars subjected to a concentric axial compressive load. Laboratory experiments were planned to evaluate and compare the effect of different design parameters on the structural behavior of column specimens based on experiments and finite element (FE) analysis. The experimental variables were (i) concrete types, i.e., conventional concrete (CC) and fiber-reinforced concrete (FC), (ii) longitudinal reinforcement types, i.e., steel and GFRP bars, and (iii) transverse rebar configurations, i.e., tied and spiral with different pitches. Sixteen column specimens were fabricated and categorized into four groups with respect to rebar configurations and concrete types. The results showed that the failure modes and cracking patterns of those four column groups were comparable, particularly in the pre-peak branches of load-deflection curves. Even though the average ultimate load of the columns with longitudinal GFRP bars was 17.9% less than that with longitudinal steel bars, the ductility index (DI) was 10.2% greater than their counterpart on average. The addition of steel fibers (SF) to concrete increased the axial peak load by up to 3.1% and the DI by up to 6.6% compared to their counterpart CC columns without SFs. The DI of specimens was increased by higher volumetric ratios (up to 12%) and spiral types (up to 5.5%). The concrete damage plastic (CDP) model for FC columns was updated in the finite element software ABAQUS 6.14. Finally, a new simple equation was theoretically proposed to predict the axial capacity of specimens by considering the inclusion of longitudinal GFRP rebars, volumetric ratio, and steel spiral/hoop ties. Good agreement between the proposed model predictions and the experimental/numerical results was observed.

Training Demand Prediction Models by Decision Error for Two-Stage Lot-Sizing Problems

Demand prediction to support appropriate production decisions is being actively studied. Many prediction models are designed to minimize the prediction error, which is measured by determining the difference between the predicted and ground-truth demand. However, these models ignore the effect of the prediction error on downstream production decisions. This prompted our study, which focuses on demand prediction models for two-stage uncapacitated lot-sizing problems. In this paper, we present a prediction model that minimizes the decision error, which is measured by the optimization objective of lot-sizing problems. Our model mitigates the impact of prediction errors by leveraging the structure of the lot-sizing problems. To enhance the ability to accommodate imperfect data, such as data based on inaccurate information, we subsequently extend the prediction models to distributionally robust versions. We consider the worst-case formulation in the feature space to enhance the robustness of the model to data imperfection. Numerical experiments demonstrate that the proposed prediction models are significantly superior to the traditional prediction methods when the model being trained is misspecified. In addition, the robust extension enables the models to train well on imperfect datasets while requiring less training data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study is motivated by the two-stage lot-sizing problem in manufacturing systems which involves the determination of the time at which (the setup decisions) to produce before the demand is revealed, and the production quantity decisions are adjusted during production processing. Predicting the demand properly while making the set-up decisions helps to decrease the production cost. This paper proposes a novel method that aims to minimize the total production cost to train demand prediction models. Practitioners can deploy the proposed method to train popular prediction models including linear prediction, decision tree, and neural network models. It is shown that the production cost associated with the decisions based on our models is lower than that associated with the traditional prediction models. Furthermore, our method enhances the ability of the model to handle model misspecification and imperfect data. Practitioners can apply our method to overcome the problems caused by imperfect data such as those containing contaminated and inaccurate measurements.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.

An Analytic Model for COVID-19 Cases in Iran and Its Neighbors Using Deep Learning and Time Series Methods

Since the pandemic of the coronavirus (COVID-19) in 2019, it has rapidly become a major global health concern. Various mutations and rapid spread of the virus, a lack of specific treatment, and limited hospital facilities highlight the importance of anticipation, risk analysis, and timely treatment. The use of mathematical models, artificial intelligence, and simulation methods are effective tools in predicting the spread and providing effective solutions to prevent virus transmission. Analysis and forecasting require an integrated model to cover different aspects of the problem and use different methods to obtain appropriate results. In this research, a proposed model for analysis and prediction of COVID-19 cases in Iran and neighboring countries is presented. The performance of mathematical and deep learning models in the proposed model has been evaluated using data from Johns Hopkins University from January 29, 2020, to April 30, 2021. Evaluation of the predictive outcomes of daily cases was performed using RMSE criteria. Then, the effect of the trend of cases in neighboring countries of Iran on the rate of new cases in this country has been studied. These models can help governments predict the number of infections to provide the necessary solutions and prevent a new wave of the virus.

Investigation on machining of GFRP through ANOVA, DFA, and CoCoSo method combined with Taguchi approach

Polymer-based composites (GFRP) are increasingly used in the mechanical industry due to their cost-profitability, durability, and good mechanical properties. In the present study, a metal carbide tool was employed to manufacture polyamide (PA66-GF30%) specimen in a dry environment. The impact of variations in cutting process parameters on the responses studied is investigated using two experimental techniques. The first is the OFAT approach. For the second technique (multifactorial), the effects of input parameters on technological parameters (Ra, Fz, MRR, and Pc) are quantified aided by the ANOVA method using an experimental design based on Taguchi’s L9 (3 × 3) orthogonal matrix. Based on the RSM methodology, the results were processed statistically to propose prediction models for the different outputs. In addition, a single-objective optimization study of Taguchi’s and a multi-objective optimization of the operating conditions using the classification method based on CoCoSo as well as the DF Approach according to two desired objectives were carried out. The first objective of this study concerns the minimization of (Ra, Fz, and Pc), and the second one considers the minimization of (Ra, Fz, and Pc) at the same time as maximizing (MRR). Finally, the surface roughness criteria (Ra and Sa) of PA66-GF30% were evaluated to study the effect of the feed (f) on the machined surface topography. The findings are of capital importance in that they provide the required and correct information about the working conditions of composite polymers. According to “OFAT” results, feed (f) is the predominant factor influencing roughness (Ra). Temperature in the cutting zone rises with increasing cutting conditions (Vc, f and ap). Variations in cutting conditions affect the morphology of the chip produced. The roughness criteria (Ra and Sa) results clearly show that the topography of the PA66-GF30% surface changes as (f) changes. Also, optimization of all performance parameters simultaneously indicates that the DFA offers the best combination of parameters: Vc = 187.50 m × min−1, f = 0.096 mm × rev−1, and ap = 0.866 mm, leading to the minimization of (Ra) and maximization of (MRR), with respective values of 1.24 µm and 15.614 cm3 × min−1. Furthermore, the CoCoSo method favors the minimization of (Fz and Pc) with values of 20.18 N and 69.285 W, respectively.

Improving dengue fever predictions in Taiwan based on feature selection and random forests

BackgroundDengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan.ResultsThis study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter < 10 µm (PM10), PM2.5, and an ultraviolet index, for predicting dengue fever occurrence. The collected dataset was randomly divided into an 80% training set and a 20% test set. The experimental results showed that the random forests achieved an area under the receiver operating characteristic curve of 0.9547 for the test set, which was the best compared with the other machine learning algorithms. In addition, the temperature was the most important factor in our variable importance analysis, and it showed a positive effect on dengue fever at < 30 °C but had less of an effect at > 30 °C. The AQIs were not as important as temperature, but one was selected in the process of filtering the variables and showed a certain influence on the final results.ConclusionsOur study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.

Probabilistic fatigue life prediction of corroded PC beams considering spatial variability of pitting corrosion

Pitting corrosion intensifies local stress in materials, accelerating the fatigue crack initiation and propagation at the pit root. The randomly distributed corrosion pits can result in fatigue fractures in rebars and wires at non-critical sections, which further increases the fatigue failure probability of structures. However, previous studies have scarcely taken this into account. Here, we propose a novel model for fatigue life prediction of corroded post-tensioned prestressed concrete (PC) beams, considering the spatial variability of pitting corrosion. First, a Weibull distribution model of the pitting factor is developed based on the 3D laser scanning method. Then, the 3D non-Gaussian random field of the pitting factor in PC beams is generated using the inverse Nataf transformation. Next, the 3D pit-induced stress concentration is modeled using pit shape ratios. Following that, the initiation and propagation of fatigue cracks in pitting corroded wires and rebars are considered by the equivalent initial flaw size methodology. Finally, the fatigue analyses of the pitting corroded wires and rebars in all beam elements are conducted step by step until the load-bearing capacity of PC beams becomes lower than the applied load. The proposed prediction model is validated using the experimental data. The results reveal that neglecting the spatial variability of pitting corrosion overestimates the fatigue life of corroded PC beams, especially under high corrosion degrees.

Machine Learning-Based Backpressure Unstart Prediction and Warning Method for Combined Cycle Engine Hypersonic Inlet-Oriented Wide Speed Range

Inlet unstart prediction and warning are strictly crucial to the operation of hypersonic engines, especially for combined cycle engines where implementation across a wide speed range poses significant challenges. This paper proposes a realization method that involves constructing the conditions of critical backpressure ratios for the inlet unstart and unstart warning states within a wide speed range and establishing the backpressure prediction models for each engine mode. The detection of the unstart and unstart warning states is achieved by predicting the backpressure ratio at the exit of the isolator and comparing it to the critical backpressure ratios. To achieve this, numerical simulations for a three-dimensional inward-turning multiducted hypersonic combined inlet at various Mach numbers and backpressure ratios are carried out to obtain the dataset of surface pressure. A 10-fold cross-validation support vector machine (10-CV SVM) is used to solve the unstart boundary of surface pressure, and an unstart margin is set to determine the unstart warning boundary. A back propagation (BP) neural network is constructed to estimate the critical backpressure ratios at each working point within a wide speed range. The data information of surface pressure on the boundaries is used as the input for the predictions. The overall average regression correlation coefficient approaches 0.99 on the test dataset at each working point. The backpressure prediction models are established by the one-dimensional convolutional neural network (1D-CNN). Only 2 to 4 measurement points of surface pressure are considered for cross-validation evaluation, and the mean absolute percentage error is between 4% and 8% with the average prediction time not exceeding 2 ms. Finally, the proposed method and prediction models are validated by wind tunnel experimental data.

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model’s predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production.

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change.

Predicting COVID-19 cases in Belo Horizonte-Brazil taking into account mobility and vaccination issues.

The pandemic caused millions of deaths around the world and forced governments to take drastic measures to reduce the spread of Coronavirus. Understanding the impact of social distancing measures on urban mobility and the number of COVID-19 cases allows governments to change public policies according to the evolution of the pandemic and plan ahead. Given the increasing rates of vaccination worldwide, immunization data may also represent an important predictor of COVID-19 cases. This study investigates the impact of urban mobility and vaccination upon COVID-19 cases in Belo Horizonte, Brazil using Prophet and ARIMA models to predict future outcomes. The developed models generated projections fairly close to real numbers, and some inferences were drawn through experimentation. Brazil became the epicenter of the COVID-19 epidemic shortly after the first case was officially registered on February 25th, 2020. In response, several municipalities adopted lockdown (total or partial) measures to minimize the risk of new infections. Here, we propose prediction models which take into account mobility and vaccination data to predict new COVID-19 cases.

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the concentration of ammonia nitrogen is too high, it will seriously affect the survival state of aquaculture. Therefore, prediction and control of the ammonia nitrogen concentration in advance is essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) and the Long Short-Term Memory neural network (LSTM) to predict ammonia nitrogen concentration in mariculture. Firstly, the weight assignment strategy was improved, and the number of correction iterations was introduced to retard the shortcomings of data error accumulation caused by the AdaBoost basic algorithm. Then, the XAdaBoost algorithm generated and combined several LSTM su-models to predict the ammonia nitrogen concentration. Finally, there were two experiments conducted to verify the effectiveness of the proposed prediction model. In the ammonia nitrogen concentration prediction experiment, compared with the LSTM and other comparison models, the RMSE of the XAdaBoost–LSTM model was reduced by about 0.89–2.82%, the MAE was reduced by about 0.72–2.47%, and the MAPE was reduced by about 8.69–18.39%. In the model stability experiment, the RMSE, MAE, and MAPE of the XAdaBoost–LSTM model decreased by about 1–1.5%, 0.7–1.7%, and 7–14%. From these two experiments, the evaluation indexes of the XAdaBoost–LSTM model were superior to the comparison models, which proves that the model has good prediction accuracy and stability and lays a foundation for monitoring and regulating the change of ammonia nitrogen concentration in the future.

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately.

Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters

Aiming to improve on the low accuracy of current transmission line icing prediction models and ignoring the objective law of icing of transmission lines, a transmission line icing prediction model considering the effect of transmission line tension on the bundle of icing thickness is proposed, based on a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Firstly, the finite element calculation model of the conductor and insulator system was established, and the change rule between transmission line tension and icing thickness was studied. Then, the convolutional neural network and bidirectional gated recurrent unit were used to construct a transmission line icing thickness prediction model The model incorporated a weighted fusion of soft−dynamic time warping (Soft−DTW) and the icing change rule as the loss function. Optimal weights were determined through the utilization of the grid search algorithm and cross−validation, contributing to an enhancement of the model’s generalization capabilities and a reduction in prediction errors. The results indicate that the proposed prediction model can consider the impact of line operating parameters, avoiding the shortcomings of prediction results conflicting with actual physical laws. Compared with traditional non−mechanical models, the proposed model showed reductions in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 0.26–0.51%, 0.24–0.44%, and 5.77–13.33%, respectively, while the coefficient of determination (R2) increased by 0.07–0.13.

A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction

With the continuous global increase in installed wind power capacity and subsequent surge in power generation, the contradiction between the safe operation of the grid and the efficient consumption of new energy after large-scale grid connection has become increasingly prominent. This paper presents a hybrid model prediction method to further improve the stability and accuracy of wind power prediction. Firstly, variational modal decomposition (VMD) optimized by the coati optimization algorithm (COA) is employed to decompose original wind power, mitigating the non-stationary characteristics of the power sequence. Subsequently, permutation entropy (PE) is used to recombine the decomposed components, and the combined feature selection method is achieved by integrating the Spearman correlation coefficient (SCC) and the autocorrelation function (ACF). Then, the multivariate combined model is constructed, and the improved multi-objective coati optimization algorithm (IMOCOA) determines the weight coefficients of each model to enhance the performance of the hybrid model. Finally, research and analysis are conducted from multiple scenarios and time scales using historical operating data from a wind farm in Xinjiang. The experimental results show that the proposed prediction model effectively improves the accuracy and stability of the wind power prediction compared with other popular prediction models.

Modeling damage caused by combined thermal and traffic loading using viscoelastic continuum damage theory

Various engineering models have been proposed to mitigate fatigue cracking associated with thermal and traffic loadings, which are the two primary fatigue cracking mechanisms that negatively affect the service life of asphalt pavement. Thermal fatigue cracking is induced by cycles of temperature changes with relatively long time periods, which can be regarded as slow frequency sinusoidal loading. Traffic fatigue cracking is induced by numerous vehicle load repetitions and is often characterized using fast frequency sinusoidal loading. These two loading types occur simultaneously in the field. In order to investigate how to properly model the damage due to these two loading types, a set of tests that vary the load, frequency, and mode of testing was designed to mimic the scenarios that asphalt pavements experience under thermal loading and traffic loading separately and under the combined and simultaneous effects of both thermal and traffic loading. The viscoelastic continuum damage (VECD) model was applied to simulate the changes in material integrity under each condition. After determining the appropriate configuration for the proposed prediction model, the material integrity predictions were found to be accurate for the fast frequency tests but less accurate for the slow frequency tests. This difference is attributable to plasticity being a much more significant factor under slow frequency conditions than under fast frequency conditions. For the tests combining both fast and slow frequency conditions, the material integrity results oscillated continuously. It was postulated that these oscillations were due to the recovery mechanisms of asphalt concrete such as nonlinearity and healing. When the simplified viscoelastic continuum damage (S-VECD) model was applied, which does not consider the material’s recovery characteristics, the predicted material integrity was able to match the overall trend observed in the tests. Ultimately, a new way to model combined damage simultaneously under two cracking mechanisms (thermal fatigue cracking and traffic fatigue cracking) is introduced in this paper.