Predictive Power Of Model Research Articles

Wind Power Prediction Based on The SENet-CNN-LSTM Model

aims: To improve the accuracy of wind power prediction background: In recent years, wind power has developed rapidly nationwide and worldwide.How to improve the prediction precision and accuracy of wind power has become a research hotspot in this field objective: This paper aims to construct a wind power forecasting system based on SENet-CNN-LSTM to improve the accuracy and stability of wind power forecasting method: Firstly, the isolated forest algorithm is used to detect abnormal values in wind power historical data, and the linear interpolation method is used to fill in the missing data. Secondly, CNN is used to extract the spatio-temporal characteristics of wind power data, SENet is used to assign different weights to the extracted feature information, and LSTM’s unique gating mechanism is used to memorize and forget the information. Finally, taking the measured historical data of a wind power farm as the sample, six algorithms including CNN, LSTM, CNN-LSTM, SENet-CNN, SENet-LSTM and SENet-CNN-LSTM are used to predict the example result: The prediction results show that the RMSE, MAE and MAPE of SENet-CNN-LSTM are significantly reduced compared with the other five prediction models conclusion: The wind power prediction model based on SENet-CNN-LSTM has high prediction accuracy and good performance other: There is no.

Study of Wind Power Prediction in ELM Based on Improved SSA

This paper proposes a short‐term wind power prediction model based on the improved Sparrow Search Algorithm (SSA) and Extreme Learning Machine(ELM) for anomalous wind power information from wind farms. The objective is to enhance the accuracy of short‐term wind power prediction. The model employs the extraction of features utilizing raw wind power history data from wind farms, in conjunction with the application of Variable Importance in Projection indices in Partial Least Squares (PLS‐VIP). As the ELM network model is susceptible to the influence of randomly generated input weights and thresholds at the outset of training, a solution is proposed whereby the input weights and thresholds of the ELM are optimized using SSA. The optimal weights and thresholds identified by SSA are then applied to the ELM model, thus forming the SSA‐ELM model. To address the limitations of traditional SSA, namely its susceptibility to local optimal solutions and poor global search ability, an improved SSA‐ELM algorithm is proposed. The improved SSA‐ELM algorithm introduces chaotic sequences and an exchange learning strategy to the original SSA. The rationale behind incorporating chaotic sequences is to enhance the quality of the initial solution, ensuring a more uniform distribution of sparrow positions and, consequently, a more diverse sparrow population. This, in turn, enables the algorithm to achieve a more effective global search capability through the utilization of the exchange learning strategy. Subsequently, all the data are fed into the SSA‐ELM model for prediction purposes. The simulation results demonstrate that the model exhibits enhanced prediction accuracy and improved practical applicability in wind power prediction. © 2025 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A deep learning based short-term wind power prediction model with Fourier self-attention mechanism

A deep learning based short-term wind power prediction model with Fourier self-attention mechanism

Melodic Movements: The Role of Music in Shaping Sport Performance and Psychological Responses

The application of music in sport can enhance training and competition performance. This study explored the effects of different music genres on students' emotional, psychological, and athletic performance. Motivational music (e.g., blues, jazz, classical) has reflective and complex qualities, with a tempo of 60-80 beats per minute; high-tempo music (e.g., rock, heavy metal, alternative) has intense and rebellious characteristics, with a tempo of 120-140 beats per minute; upbeat music (e.g., pop music, New German Wave), film music, and popular German music have cheerful and traditional qualities, with a tempo of 100-130 beats per minute; rhythmic music (e.g., soul/R&B, rap/hip-hop, and electronic music), have strong rhythmic features, with a tempo of 80-100 beats per minute; and self-selected music is chosen by the participants based on preference. Using a structured questionnaire, data were collected from 400 students at Shandong Sport University (50.8% male, 49.3% female, ages 18-21) to assess demographics, music preferences (Short Test of Music Preferences), psychological responses (Sport Emotions Questionnaire (SEQ) and Attention Questionnaire (AQ-RARC), sport motivation (adapted from Gill et al., 1983), and physical performance (International Fitness Inventory). Results from the PLS-SEM analysis indicated a composite reliability (CR ≥0.7) and an average variance extracted (AVE ≥0.5), meeting the Fornell-Larcker criteria for the model's reliability and validity. Model fit indices (SRMR, RMSEA < 0.08; CFI > 0.90) and path coefficient (t-value > 1.96), confirmed significance, with an R² value > 0.3 and a Q² value > 0, demonstrating the model's explanatory and predictive power. Findings suggest that different music genres significantly enhance students' athletic performance and psychological well-being.

Experimental Testing of the Theoretically Predicted Magnetic Properties for Kagomé Compounds in the Li-Fe-Ge System.

Investigating material properties is essential to assessing their application potential. While computational methods allow for a fast prediction of the material structure and properties, experimental validation is essential to determining the ultimate material potential. Herein, we report the synthesis and experimental magnetic properties of three previously reported Kagome compounds in the Li-Fe-Ge system. LiFe6Ge4, LiFe6Ge5, and LiFe6Ge6 were predicted to have ferromagnetic or antiferromagnetic ground states. The hydride route that replaces the ductile Li metal with salt-like LiH proved to be an excellent alternative for the facile synthesis of the Li-Fe-Ge powders with appreciable purity, permitting the investigation of their bulk magnetic properties. Magnetometry below room temperature and room-temperature 57Fe Mössbauer spectroscopy collectively indicate an antiferromagnetic ground state for the three compounds with ordering temperatures above 300 K, contrary to the prediction of ferromagnetic ground states. Moreover, Mössbauer spectroscopy reveals a magnetization of 1.1-1.3 μB/Fe atom for the Li-Fe-Ge compounds, while higher moments of 1.63-2.90 μB/Fe atom were theoretically predicted. Experimental (in)validation addresses the issue of inaccuracy in determining material properties in silico only and helps to improve the prediction power of the computational models. This work underlines that the contribution of experimentalists continues to be valuable for the accurate determination of structure-property relationships in solid-state materials.

Enhancing Kernel-based Model Predictive Power Through Enhanced Relief-based Algorithm for the Early Detection of Alcohol Use Disorder Among Secondary Students

Enhancing Kernel-based Model Predictive Power Through Enhanced Relief-based Algorithm for the Early Detection of Alcohol Use Disorder Among Secondary Students

Research on Low-Voltage Ride-Through and Intelligent Optimization Control of Wind Turbines Based on Hybrid Power Prediction Models

This paper proposes a dual-loop back-to-back converter coordination control scheme with a DC-side voltage as the primary control target, along with a CROW unloading control strategy for low voltage ride-through (LVRT) capability enhancement. The feasibility and effectiveness of the proposed system topology and control strategy are verified through MATLAB/Simulink simulations. Furthermore, a hybrid short-term wind power prediction model based on data-driven and deep learning techniques (CEEMDAN-CNN-Transformer-XGBoost) is introduced in the wind turbine control system. The coordination control strategy seamlessly integrates wind power prediction, pitch angle adjustment, and the control system, embodying a predictive-driven intelligent optimization control approach. This method significantly improves prediction accuracy and stability, theoretically reduces unnecessary pitch angle adjustments, lowers mechanical stress, and enhances system adaptability in complex operating conditions. The research findings provide a valuable theoretical foundation and technical reference for the intelligent and efficient operation of wind power generation systems.

Data Decomposition Modeling Based on Improved Dung Beetle Optimization Algorithm for Wind Power Prediction

Accurate wind power forecasting is essential for maintaining the stability of a power system and enhancing scheduling efficiency in the power sector. To enhance prediction accuracy, this paper presents a hybrid wind power prediction model that integrates the improved complementary ensemble empirical mode decomposition (ICEEMDAN), the RIME optimization algorithm (RIME), sample entropy (SE), the improved dung beetle optimization (IDBO) algorithm, the bidirectional long short-term memory (BiLSTM) network, and multi-head attention (MHA). In this model, RIME is utilized to improve the parameters of ICEEMDAN, reducing data decomposition complexity and effectively capturing the original data information. The IDBO algorithm is then utilized to improve the hyperparameters of the MHA-BiLSTM model. The proposed RIME-ICEEMDAN-IDBO-MHA-BiLSTM model is contrasted with ten others in ablation experiments to validate its performance. The experimental findings prove that the proposed model achieves MAPE values of 5.2%, 6.3%, 8.3%, and 5.8% across four datasets, confirming its superior predictive performance and higher accuracy.

Optimizing Hepatitis C Virus Inhibitor Identification with LightGBM and Tree-structured Parzen Estimator Sampling

Identifying potent inhibitors against the Hepatitis C Virus (HCV) is crucial due to the continuous emergence of drug-resistant strains. Traditional drug discovery methods, including high-throughput screening, are often resource-intensive and time-consuming. Machine Learning (ML) approaches, particularly Quantitative Structure-Activity Relationship modeling, have been increasingly adopted to address this. This study utilized LightGBM, an efficient gradient-boosting framework, to predict the activity of potential HCV inhibitors. Additionally, the Tree-structured Parzen Estimator (TPE) was employed for hyperparameter optimization to enhance model performance. The optimized LightGBM-TPE model outperformed other ML models, including standard LightGBM, XGBoost, Random Forest, K-Nearest Neighbors, and Support Vector Machines, achieving an accuracy of 86.27%, a precision of 85.47%, a recall of 87.50%, a specificity of 85.03%, and an F1-score of 86.47%. Feature importance analysis identified critical molecular descriptors contributing to the model's predictive power. The results underscore the potential of advanced ML techniques and robust optimization methods to accelerate drug discovery, particularly for challenging targets such as HCV.

A comprehensive evaluation of machine learning and deep learning algorithms for wind speed and power prediction

Accurate wind speed and power predictions are crucial for renewable wind energy applications. This study compares and evaluates twelve machine learning (ML) and deep learning (DL) algorithms, including single and ensemble models across various time scales from 10 min to a day and a half ahead with a particular focus on ensemble prediction algorithms. Moreover, the study proposes a wind speed and power prediction system where the outcome of the wind speed prediction (WSP) model serves as input for the wind power prediction (WPP) model. Several evaluation metrics, such as mean absolute percentage error (MAPE) and mean square error (MSE) were calculated to benchmark different model accuracies. For WSP, the extremely randomized trees, decision tree, and bagging ensemble algorithms demonstrated high accuracy across different time scales where the MAPE ranged from 3.4% to 9.2%, the MSE ranged from 0.17 to 1.15, and the adjusted coefficient of determination ranged from 94% to 99%. For WPP, bagging ensemble algorithms and extremely randomized trees were also effective for predicting different time scales where the MAPE ranged from 4.12% to 11.7% and the MSE ranged from 10945 to 2.4. Ensemble ML algorithms provide better and more accurate results than single ML algorithms. The extreme gradient boosting model showed relatively small computational time and memory according to computational cost. Moreover, this study conducted a sensitivity analysis where air pressure, wind vane, and humidity were the key predictors for WSP and WPP.

A binary choice model for adoption of an emerging travel mode with unique service features

In the era of emerging technologies, the transportation system is witnessing the introduction of innovative mobility services, such as autonomous vehicles, which possess unique service features that cannot be seen from conventional travel modes. To facilitate the understanding of the behavioral impacts and the adoption of innovative mobilities, a novel binary weibit model with an oddball alternative (BW-O) is developed for the binary choice between conventional and emerging mobilities. The BW-O model explicitly considers the unprecedented (or unique) service features of emerging travel modes while retaining the closed-form choice probability. This study empirically illustrates the application of the BW-O model in the mode choice context. The desirable properties of the BW-O model compared to the existing binary choice models are discussed both theoretically and empirically. In the binary mode choice problem with an emerging travel mode, the unique service features of the emerging mode can lead to the “oddball” effect and “superstar” effect, which play a critical role in the travel behavior and mode adoption. The BW-O model inherently captures both effects by considering a higher perception variance for the emerging mode and asymmetric choice probabilities between different modes. Thus, as revealed by the empirical results, the BW-O model outperforms the basic binary weibit model in terms of both model fit and predictive power. The developed BW-O model is not only applicable to the mode choice problem in transportation systems, but also opens a door for more general class-imbalanced binary choice contexts where an alternative has additional attractiveness and asymmetric choice probability.

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Human essential gene identification based on feature fusion and feature screening.

Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K-spaced Nucleic Acid Pairs, and Z-curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single-feature-based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.

A Transferable Federated Learning Approach for Wind Power Prediction Based on Active Privacy Clustering and Knowledge Merge

As wind power continues to develop, advancing wind power prediction becomes more and more crucial. This study focuses on advancing wind power prediction by addressing data privacy and enhancing model applicability at multi-spatial scales, from individual turbines to entire farms. Traditional methods are typically confined to a single scale, lacking flexibility in application, requiring extensive data from farms, which potentially compromises energy data privacy. To tackle these challenges, we introduce an innovative Divide-Merge Federated Learning with Active Private Clustering (D-M APCFed) approach. This approach strategically employs federated learning to train models within privacy-preserving boundary, overcoming the adverse effects of wind power data heterogeneity through a novel APC method and knowledge merge technique. The primary innovation of this study is a scalable and accurate wind power prediction model that operates effectively at multi-spatial scales while safeguarding energy data privacy. In case study of two spatial scales, the D-M APCFed approach achieves an average prediction accuracy of 87.11% in the twelve federated farms and 81.69% in the twenty federated turbines. This approach enables a more generalized model through the secure use of data from diverse sources at multi-spatial scales, enhancing prediction accuracy and ensuring the confidentiality of sensitive information.

Predicting Counterproductive Work Behaviors: Examining the Role of Spiritual Intelligence and Personality Traits in Public and Private Sector Organizations

Counterproductive work behaviors are intentional and harmful behaviors directed either towards the organization or towards its people. This study attempts to examine the role of two antecedent variables, spiritual intelligence (SQ), and personality based on the Big Five Personality dimensions (extraversion, agreeableness, conscientiousness, neuroticism and openness) in predicting the occurrence of counterproductive work behaviors. Two dimensions of counterproductive work behaviors: rating and self-indulgence were used in this study based on the tool developed by Jain & Singh (2020). A sample of 351 employees working in both public (170) and private (181) sector organizations in India was taken for the purpose of the study. Mean, correlational analysis and multiple hierarchical regressions were carried out to test the hypotheses. Significant results were found for both personality and spiritual intelligence (SQ) in predicting counterproductive work behaviors across both public and private sector organizations. The two antecedent variables significantly improved the model's predictive power, for both public and private sector organizations, though to a lesser extent in the private sector. No difference was found based on gender.

The systemic immune-inflammation index and systemic inflammation response index are useful for predicting mortality in patients with diabetic nephropathy

BackgroundThis study investigated the correlation between the systemic immune-inflammation index (SII) and the systemic inflammation response index (SIRI) and all-cause, cardiovascular, and kidney disease mortality in patients with diabetic nephropathy (DN). It aimed to provide a new predictive assessment tool for the clinic and a scientific basis for managing inflammation in DN.MethodsThe data utilized in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database, spanning 1999 to 2018. A total of 2641 patients diagnosed with DN were included in the analysis. The association between SII and SIRI levels and mortality in patients with DN was investigated using multivariate Cox proportional risk regression models. These relationships were further validated by Kaplan-Meier survival curves and restricted cubic spline (RCS) modeling, and subgroup analyses were performed to explore the heterogeneity among different characteristic subgroups.ResultsThe multivariate Cox regression analysis indicated that SII and SIRI levels were independently associated with all-cause mortality and cardiovascular mortality in patients with DN. SIRI levels were found to be an independently associated factor with kidney disease mortality in patients with DN. Patients in the highest quartile of SII and SIRI exhibited a 1.49-fold and 1.62-fold increased risk of all-cause mortality, respectively, compared to patients in the lowest quartile. The risk of cardiovascular mortality was 1.31 and 1.73 times higher than that in patients in the lowest quartile, respectively. The risk of kidney disease mortality in patients in the highest quartile of SIRI was 2.74 times higher than that in patients in the lowest quartile. Kaplan-Meier survival curve and RCS analyses further confirmed the positive association between SII and SIRI and mortality and a significant nonlinear relationship between SII and all-cause mortality. The SII and SIRI indices offer incremental value in model predictive power for mortality in patients with DN. Subgroup analyses demonstrated that the correlation between SII and SIRI and mortality risk was stable but heterogeneous across different subgroups.ConclusionSII and SIRI can be utilized as biomarkers for forecasting the likelihood of all-cause and cardiovascular mortality in patients with DN.

Random forest models reveal academic and financial factors outweigh demographics in predicting completion of a year-round veterinary program.

The purpose of this study was to develop random forest classifier models (a type of supervised machine learning algorithm) that could (1) predict students who will or will not complete the DVM degree requirements and (2) identify the top predictors for academic success and completion of the DVM degree. The study utilized Ross University School of Veterinary Medicine student records from 2013 to 2022. Twenty-four variables encompassing demographic (eg, age, race), academic (eg, grade point average), and financial aid (eg, outstanding balances) data were assessed in 11 cross-validated random forest machine learning models. One model was built assessing all years of data and 10 individual models were developed for each enrollment year to compare how the top predictors of success varied among the years. Consistently, only academic and financial factors were identified as being features of importance (predictors) in all models. Demographic factors such as race were not important for predicting student success. All models performed very well to excellently based on multiple performance metrics including accuracy, ranging from 96.1% to 99%, and the areas under the receiver operating characteristic curves, ranging from 98.1% to 99.9%. The random forest algorithm is a powerful machine learning prediction model that performs well with veterinary student academic records and is customizable such that variables important to each veterinary school's student population can be assessed. Identifying predictors of success as well as at-risk students is essential for providing targeted curricular interventions to increase retention and achieve timely completion of a DVM degree.

How Eco-Conscious Choices are Shaping Generation Z’S Tumbler Market

This study reveals how consumer and environmental trends are affecting Indonesia's tumbler market, emphasizing the eco-conscious habits of Generation Z. Environmental issues including pollution, deforestation, and climate change have made sustainable materials and methods even more important. As much as 105 Gen Z taken a role as a respondent in this study, by filled out a set of a questionnaire. As a result of product improvements that meet Gen Z's need for sustainability, the Indonesian tumbler industry has grown quite significantly. The study used a quantitative technique to examine how the marketing mix—which includes product, price, site, and promotion—affects the Generation Z’s purchasing decisions of, who were primarily university students in various provinces in Indonesia. The results show that while price and location have less impact on purchasing decisions, product characteristics and advertising tactics do. Regression analysis supports the model's prediction power, while validity and reliability tests validate the power of measuring instruments. This study sheds light on how important strategic marketing is to attract eco-conscious customers and offers guidance to companies adjusting to Indonesia's growing green consumer culture.

Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach

Abstract. This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analysis revealed that the rainfall at the road segment level could be sufficiently represented by that recorded by nearest sensors. Within a 4 d window, the rainfall in the current day and 3 d prior played a more influential role in predicting road closure. The differential outcomes due to distinct regional features were explained. Finally, a watershed delineation approach substantially improved the model's predictive power and sensitivity.

A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.