Predictive Power Of Model Research Articles

Социодемографические детерминанты индивидуальной переоценки склонности к проэкологическому поведению

<p>The overestimating effect of one's tendency and contribution to pro-environmental practices, and underestimating the pro-environmental behavior of others has been noted recently in cross-country studies. The present work is devoted to a detailed post-hoc analysis of the available empirical data on this effect on the Russian sample (N = 109, M<sub>age</sub> = 28.3, SD<sub>age</sub> = 10.4, 39% men) and identification of the role of socio-demographic variables (gender, age, income, employment, size of city) as its predictors. The analysis showed that the nature of the contribution of the variables under consideration to the respondents' self-estimation and estimation of others with respect to pro-environmental behavior is non-consistent, which is in line with recent studies. On average for all models and statistically significant determinants, the standardized coefficient ???? does not exceed ∓0.25, which formally corresponds to a small effect size. However, given the empirical distribution of effect size typical of social psychology, it is more likely to be medium effects. The prediction power of linear models based on individual socio-demographic characteristics also turns out to be low, the coefficient of determination varies from 0.02 to 0.11. Therefore, it is acceptable to speak about the considered variables as predictors except in relation to resource saving and eco-mobility. The conclusion is made that along with socio-demographic determinants it is necessary to include other predictors in the design of experiments and investigate them more deeply.</p>

High-Efficient Direct Power Control Scheme Using Predictive Virtual Flux for Three-Phase Active Rectifiers

In recent years, the pulse-width-modulation (PWM) converter has been found to have extensive applications in renewable energy, industrial fields, and others. The high efficiency requirement is crucial to operating a PWM rectifier in various applications, in addition to the fundamental control objectives of sinusoidal grid currents and the correct DC bus voltage. Additionally, in practical application, another issue arises when the grid voltage frequently experiences distortion, leading to a distorted grid current and a significant rise in total harmonic distortion (THD). To resolve these problems, a model predictive virtual flux-based direct power control (MPVFDPC) with improved power loss performance is proposed based on an integrated switching state predetermination strategy. The proposed MPVFDPC for PWM rectifier inherits the merits of both virtual flux control and direct power control, which have fast dynamic performance and the grid current THD is considerably decreased under distorted grid voltage states. The proposed technique aims to minimize switching loss under ideal and distorted grid voltage states by exploiting the discontinuous modulation concept by using a switching state predetermination strategy. The MPVFDPC with switching state predetermination strategy is proven by employing it in experiments as well as simulations in comparison with previous models: predictive direct power control (Conv. MPDPC) and conventional MPVFDPC (Conv. MPVFDPC). The acquired waveforms and quantitative data are employed to prove the effectiveness of the developed algorithm.

Method for wind power forecasting based on support vector machines optimized and weighted composite gray relational analysis

This study proposes a weighted composite approach for grey relational analysis (GRA) that utilizes a numerical weather prediction (NWP) and support vector machine (SVM). The approach is optimized using an improved grey wolf optimization (IGWO) algorithm. Initially, the dimension of NWP data is decreased by t-distributed stochastic neighbor embedding (t-SNE), then the weight of sample coefficients is calculated by entropy-weight method (EWM), and the weighted grey relational of data points is calculated for different weather numerical time series data. At the same time, a new weighted composite grey relational degree is formed by combining the weighted cosine similarity of NWP values of the historical day and to be measured day. The SVM’s regression power prediction model is constructed by the time series data. To improve the accuracy of the system’s predictions, the grey relational time series data is chosen as the input variable for the SVM, and the influence parameters of the ideal SVM are discovered using the IGWO technique. According to the simulated prediction and analysis based on NWP, it can be observed that the proposed method in this study significantly improves the prediction accuracy of the data. Specifically, evaluation metrics such as root mean squared error (RMSE), regression correlation coefficient (r2), mean absolute error (MAE) and mean absolute percent error (MAPE) all show corresponding enhancements, while the computational burden remains relatively low.

Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model

Accurate prediction of wind and photovoltaic (PV) power is essential to ensure the stable operation of power systems. However, the conventional power prediction methods face the challenges in capturing the stochastic fluctuations of wind and PV power. A wind and PV power prediction method fusing the multi-stage feature extraction and a particle swarm optimization (PSO)-bidirectional long and short-term memory (BiLSTM) model is developed. To illustrate the oscillation and instability of wind and PV power, the symplectic geometry model decomposition (SGMD) is presented to decompose the feature data and obtain multiple feature sub-sequences. Due to the excessive feature decomposition using the conventional methods, such as empirical modal decomposition, the kernel principal component analysis is utilized to reduce the dimensionality of the nonlinear sub-sequences of the wind and PV power. Compared to the traditional LSTM that ignores the inverse-time correlation between power and meteorological features and is sensitive to the model hyper-parameters, a power prediction model based on PSO-BiLSTM is developed. Using the power data from a power station in Xinjiang Province, China, as an example, the experimental results show that wind and PV power can be accurately forecasted using the developed method with high prediction accuracies of 97.9 % and 98.5 %, respectively.

Efficient shrinkage temporal convolutional network model for photovoltaic power prediction

Power stations operating on PhotoVoltaic (PV) power generation are challenged by demand forecasting as PV power generation is random and intermittent. This paper presents an Efficient Shrinkage Temporal Convolutional Network (ESTCN) model, which combines the Temporal Convolutional Network (TCN) and an improved Deep Residual Shrinkage Network (DRSN) to forecast PV power output. First, the attention sub-network of the DRSN is improved, the fully connected layer of the sub-network is canceled, and feature extraction is done directly on the model features following global average pooling using a one-dimensional convolution to obtain cross-channel interaction and increased model efficiency. Next, an improved attention mechanism and adaptive soft thresholding are introduced into TCN to automatically determine the noise threshold to address the issue of information weight dispersion caused by redundant information in the input samples. By incorporating dilated causal convolution, attention module, soft thresholding, and residual connection, the ESTCN model is formed and is shown to enhance PV power output prediction with only a minimal increase in the number of parameters. The power station in Ningxia, China is adopted as a validation example with data taken from the year 2020. The fitting and prediction results of the ESTCN model are compared against the Convolutional Neural Network, Long Short-Term Memory, and TCN models. The ESTCN model yielded the following values of the evaluation metrics: Root Mean Square Error (RMSE) of 1.47 kW, Mean Absolute Error (MAE) of 0.79 kW, and coefficient of determination of 0.999. Compared to the TCN model, the prediction errors based on RMSE and MAE improved by 0.39 kW and 0.18 kW, respectively. Applying the ESTCN model to predict PV power generation of power stations in two regions in China, Ningxia and Xinjiang, shows the ESTCN model to be superior, scalable, and universal over other deep learning models.

PROMOTING BRAIN HEALTH THROUGH MACHINE LEARNING TO PREDICT STROKE RISK

Stroke is a serious disease that can lead to serious consequences if not diagnosed and treated in time. The project focuses on using machine learning algorithms to analyze different types of data, including medical history, lifestyle and physical characteristics, to create powerful predictive models. Methods include preprocessing and data cleaning, product removal, and options to improve model accuracy. Various machine learning algorithms such as decision trees, support vector machines, and neural networks will be applied and compared to determine which is best for batting prediction [1]. The model will be trained with historical data and its performance will be evaluated with metrics such as demand, specificity and accuracy. The potential impact of this study includes early identification of individuals at high risk of stroke and provision of timely intervention and prevention. The development of effective predictive models can help reduce the burden of stroke-related morbidity and mortality. By combining architectural models with health applications, the program demonstrates the collaboration of today's technology in solving important health problems. Key Words: Brain Strokes, Early Detection, Predictive Model, Machine Learning Techniques, Medical Histories, Lifestyle Factors, Physiological Parameters, Preprocessing, Feature Extraction, Feature Selection, ML Algorithms, Healthcare Applications, Engineering Principles, Interdisciplinary Approach.

Research on Wind Power Prediction Model Based on Random Forest and SVR

Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and support vector regression (SVR) algorithm is proposed. First, in the data preprocessing stage, PCA is used for sample data denoising; then the random forest model is used to calculate the importance evaluation value of each feature to optimize the selection of feature parameters; finally, The SVR algorithm is applied for training and prediction. Experiments show that the prediction effect of the model based on random forest and SVR is excellent, the root mean square error(RMSE) is 0.086, the average absolute percentage error(MAPE) is 23.47%, and the coefficient of determination(R2) is 0.991. Compared with the traditional SVR model, the root mean square error of the method proposed in this paper is reduced by 95.9%, and the prediction accuracy and the fit of the prediction curve are significantly improved.

Physiologically Based Pharmacokinetic Modelling of UGT Substrate Drugs Lamotrigine and Raltegravir during Pregnancy

Pregnancy is associated with various physiological changes that can significantly impact the disposition of drugs. To further the insight into how pregnancy affects the pharmacokinetics of drugs at different stages, clinical studies can be simulated using Physiologically Based Pharmacokinetic modelling. PBPK modelling of drugs metabolised by Phase I enzymes (CYPs) in pregnant population models had been reported in the past, while its use in Phase II (UGTs) is not known. In this study, based on the results of a recent meta-analysis, lamotrigine (UGT1A4) and raltegravir (UGT1A1) were selected as candidate drugs, and pregnancy-specific models were developed for both using the Simcyp v.21 simulator. A middle-out strategy was used where previously published drug parameters were adapted from a minimal to a full PBPK model to allow their application for the pregnancy population models using Simcyp PBPK software. Adapted models were successfully validated against observed clinical data both qualitatively (visual overlay of plasma concentrations on graphs) and quantitatively (calculating the predicted/observed ratios for AUC, Cmax and CL as well as statistical analysis using model prediction power metrics). They were then applied to predict the PKs of both drugs in pregnancy population models. The temporal changes in maternal enzymatic activities during gestation were modelled based on in vitro data reported in literature and default relationships encoded in the Simcyp platform for UGT1A1 and UGT1A4, respectively. Our study demonstrates the successful development and validation of a PBPK model for LTG and RTG in pregnancy population models. Future work with additional UGT1A4 substrate drugs using the proposed changes in UGT1A4 activity may enable validating the pregnancy population model and its subsequent use for the prospective prediction of PK.

SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT

The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud dynamics, hinders the transition to reliable renewable energy systems. Information on future sky conditions, especially cloud coverage, holds the promise for improving PV output forecasting. Leveraging recent advances in generative artificial intelligence (AI), we introduce SkyGPT, a physics-constrained stochastic video prediction model, which predicts plausible future images of the sky using historical sky images. We show that SkyGPT can accurately capture cloud dynamics, producing highly realistic and diverse future sky images. We further demonstrate its efficacy in 15-minute-ahead probabilistic PV output forecasting using real-world power generation data from a 30-kW rooftop PV system. By coupling SkyGPT with a U-Net-based PV power prediction model, we observe superior prediction reliability and sharpness compared with several benchmark methods. The propose approach achieves a continuous ranked probability score (CRPS) of 2.81 kW, outperforming a classic convolutional neural network (CNN) baseline by 13% and the smart persistence model by 23%. The findings of this research could aid efficient and resilient management of solar electricity generation, particularly as we transition to renewable-heavy grids. The study also provides valuable insights into stochastic cloud modeling for a broad research community, encompassing fields such as solar energy meteorology and atmospheric sciences.

Machine learning models for predicting thermal desorption remediation of soils contaminated with polycyclic aromatic hydrocarbons

Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R2 = 0.90), exhibited high accuracy in predicting remediation efficiency. The hierarchical significance of the features within the RF model is elucidated as follows: heating conditions account for 52 %, contaminant properties for 28 %, and soil properties for 20 % of the model's predictive power. A comprehensive analysis suggests that practical applications should emphasize heating conditions for efficient soil remediation. This research provides a crucial reference for optimizing and implementing thermal desorption in the quest for more efficient and reliable soil remediation strategies.

Leveraging renewable energy sources for sustainable traction vehicles

In pursuit of sustainable transportation, green energy sources (GES) are taking center stage, propelling traction vehicles like tramways and trolleybuses towards a zero-emission future. Solar is the most prominent source among the available renewable sources and also for the transport system applications. To utilize photovoltaic (PV) source effectively, model predictive controller-based PV maximum power tracking algorithm is used to identify the PV parameters. Equipped with the smart energy storage system (SESS), light traction vehicles rely on the efficiency and reliability of brushless DC (BLDC) motors for smooth operation. However, BLDC motors operate at high voltages, which requires them to be connected to high voltage microgrids. Bridging the gap between ESS/SESS and the high-voltage microgrid with traditional DC-DC boost converters incurs efficiency losses and component stress. This paper tackles high-voltage needs in microgrids through innovative, efficient DC-DC boost converter designs. An innovative finite-control-set based model-predictive control (FCS MPC) controller tackles clean energy harnessing from PV and grid stability in traction applications, enabling optimized power sharing within microgrid constraints.

A STAM-LSTM model for wind power prediction with feature selection

In an effort to enhance the precision of wind power prediction, this study proposes a wind power prediction model with a secondary-weighted attention mechanism, which is based on feature selection. During the pre-processing stage, wavelet denoising is employed on the original dataset to eliminate noise and enhance the convergence rate of the model. As for model improvement, a secondary-weighted time attention mechanism-LSTM (STAM-LSTM) model is proposed. Additionally, the random forest algorithm is employed to analyse the feature correlation, leading to the best feature combination for the construction of the final input vector. In the comparative experiments, the STAM-LSTM model shows good performance and stability compared to the other nine models and three reference methods. In addition, to validate the effectiveness of feature selection, different combinations of features are entered for prediction. The results show that the model metrics reach a better level after feature selection. Finally, the effects of the STAM mechanism and the Random Forest feature screening assistance strategy, on the model are analysed through ablation experiments.

Data-Driven Approach to State of Good Repair: Predicting Rolling Stock Service Life with Machine Learning for State of Good Repair Backlog Reduction and Long-Range Replacement Cost Estimation in Small Urban and Rural Transit Systems

This paper presents a data-driven approach to address the state of good repair (SGR) in small urban and rural transit systems in the U.S. by predicting the service life of rolling stock vehicles. Achieving and maintaining public transportation rolling stock in SGR is crucial to providing safe and reliable services to riders, particularly for transit agencies utilizing federal grants that mandate asset maintenance at a full level of performance. In this context, an intelligent predictive model is proposed to analyze the transportation rolling stock, determine their backlog and current condition, predict their replacement or rehabilitation needs, and estimate the funding required for future replacements, ensuring SGR. The model utilizes historical data from retired revenue vehicles in the National Transit Database and employs machine learning techniques, specifically random forest regression and gradient boosting regression, to develop the predictive tool. By addressing the backlog of vehicles beyond their service lives and estimating replacement costs, the model provides valuable decision support for effective transit asset management. Transit agencies in small urban and rural transit systems, lacking analytical tools for service life prediction, can greatly benefit from this simple yet powerful predictive model. The approach offers a practical resource to address SGR needs, optimize investment prioritization, and enhance operational efficiency in public transit systems.

Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase in the installed capacity of wind turbines, simultaneously improving the accuracy and efficiency of large-scale wind-turbine cluster power prediction models has become a challenge. In this research, a novel hybrid power-forecasting model for wind-turbine clusters has been designed using density-based spatial clustering of applications with noise (DBSCAN) and an enhanced hunter-prey optimization algorithm (ENHPO). First, the Pearson correlation coefficient was used to select multiple variables that significantly affected the wind power. Second, multiple wind turbines are clustered into different groups using the DBSCAN clustering algorithm, and ENHPO is employed to optimize the DBSCAN parameters to strengthen the clustering performance. Finally, one wind turbine with a high correlation was selected as the representative wind turbine in each group, and power prediction was achieved using the multivariate long short-term memory. Simulation results of four datasets in different seasons show that, compared with other clustering methods, such as fuzzy C-means, balanced iterative reduction, and clustering using hierarchies, K-means, and density peak clustering, the prediction accuracy and prediction efficiency of the proposed hybrid prediction model are improved by 21.85% and 18.07%, respectively, on average. The experimental results effectively demonstrate that the designed model can enhance the accuracy and efficiency of power forecasting simultaneously.

Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous parameters that support the efficient management of the production and distribution of green energy. This article proposes multiple regression models for power prediction using the Sharda University PV dataset (2022 Edition). The proposed regression model is inspired by a unique data pre-processing technique for forecasting PV power generation. Performance metrics, namely mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2-score, and predicted vs. actual value plots, have been used to compare the performance of the different regression. Simulation results show that the multilayer perceptron regressor outperforms the other algorithms, with an RMSE of 17.870 and an R2 score of 0.9377. Feature importance analysis has been performed to determine the most significant features that influence PV power generation.

Permutation-Based Hypothesis Testing for Neural Networks

Neural networks are powerful predictive models, but they provide little insight into the nature of relationships between predictors and outcomes. Although numerous methods have been proposed to quantify the relative contributions of input features, statistical inference and hypothesis testing of feature associations remain largely unexplored. We propose a permutation-based approach to testing that uses the partial derivatives of the network output with respect to specific inputs to assess both the significance of input features and whether significant features are linearly associated with the network output. These tests, which can be flexibly applied to a variety of network architectures, enhance the explanatory power of neural networks, and combined with powerful predictive capability, extend the applicability of these models.

CNN–LSTM–AM: A power prediction model for offshore wind turbines

This study introduces a power forecasting model, the convolutional neural network (CNN)–long short-term memory (LSTM)–attention mechanism (AM) algorithm (CNN–LSTM–AM), designed to predict the power of offshore wind turbines based on data collected by a SCADA system. The model employs a timestep parameterisation approach for offshore wind turbine prediction, facilitating automatic partitioning of the training dataset and simplifying the training process. A CNN–LSTM–AM network was presented to predict the power of offshore wind turbines using signals from multiple sensors. A variable–control comparison was conducted to complete the sensitivity analysis of the sensors, which determined the most suitable sensor group for power prediction. The model achieved a maximum improvement of 13.77% in power prediction compared to existing deep learning algorithms. The results indicate that the hub and rear-end temperatures of the high-speed shaft of the gearboxes are crucial for offshore wind power prediction. Overall, the findings of this study contribute to the operation and maintenance of offshore wind turbines and the management of offshore wind farms.

Abstract 6076: Mathematical oncology in the context of early breast cancer detection

Abstract Breast cancer (BC) is the most common cancer in women globally, with approximately 94% of patients initially diagnosed with early-stage disease. However, despite the initial lack of detectable metastases and administration of subsequent treatments, 40% of these patients will develop recurrence over their lifetime. The standard screening method for BC is mammography, with a tissue biopsy to confirm diagnosis. However, only ~60% of all cases are currently diagnosed via this pathway, primarily due to the limited (86.9%) sensitivity. To demonstrate the feasibility of a liquid biopsy (LBx) test for early BC detection and assessment, we employed the third-generation, High Definition Single Cell Assay (HDSCA3.0) to detect and characterize rare cells (e.g., circulating tumor cells [CTCs]) and acellular structures (e.g., oncosomes) in patient peripheral blood samples using immunofluorescent (IF) imaging. We have previously investigated this signal in a cohort of early-stage BC patients (n = 74), late-stage BC patients (n = 26), and age- and gender-matched controls (n = 30) and observed statistically significant differences in levels of total rare cells, CTCs, and oncosomes via a manual approach. For this study, we employed a hybrid methodology, that utilizes both automatic and manual techniques, to investigate the reproducibility of the signal in an expanded validation set. Within this modified approach, we employ an outlier detection algorithm to accelerate the rare event identification and curation process (minimizing the manual portion), a separate machine learning model to classify the events based on their IF expressions, and a suite of applications to enumerate and quantify the result. After implementation, we quantified the results of the fully manual and hybrid approaches to determine the signal loss associated with scale up. Utilizing the hybrid approach, we observed similar results as with the fully manual and were able to stratify the three cohorts with high accuracy. Namely, there was an increase in the total rare cell and CTC populations in late-stage BC patients, and an increase in the oncosome population for the early-stage BC patients. To note, these patients included a subset with invasive lobular carcinoma (ILC; n = 19), which manifests in ~5-15% of BC cases and is considered “mammogram silent” due to the biological development of the disease. Additionally, employing the hybrid approach allowed for analyzing patient samples in less time while minimizing overall signal loss. In summary, we have been able to detect reproducible patterns in the enumeration of rare cells and oncosomes with high accuracy. When utilized at the patient-level, these analytes can power prediction models that are capable of stratifying disease from non-disease. The results presented here demonstrate the utility of a LBx test as a companion to screening mammography in the detection of early-stage BC across various histologies. Citation Format: Jeremy Mason, Stephanie N. Shishido, George Courcoubetis, Dean Tessone, Emmett Liljegren, Amin Naghdloo, James Hicks, Jorge J. Nieva, Peter Kuhn. Mathematical oncology in the context of early breast cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6076.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration.

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

A power dispatch allocation strategy to produce green hydrogen in a grid-integrated offshore hybrid energy system

A dedicated grid-tied offshore hybrid energy system for hydrogen production is a promising solution to unlock the full benefit of offshore wind and solar energy and realize decarbonization and sustainable energy security targets in electricity and other sectors. Current knowledge of these offshore hybrid systems is limited, particularly in the integration, component control, and allocation aspects. Therefore, a grid-integrated analytical model with a power dispatch allocation strategy between the grid and electrolyzer for the co-production of hydrogen from the offshore hybrid energy system is developed in this paper. While producing hydrogen, the proposed offshore hybrid energy system supplies a percentage of its capacity to the onshore grid facility, and the amount of the electricity is quantified based on the electricity market price and available total offshore generation. The detailed controls of each component are discussed. A case study considers a hypothetical hybrid offshore energy system of 10 MW situated in a potential offshore off the NSW of Australia based on realistic metrological data. A grid-scale proton-exchange membrane electrolyzer stack is used and a model predictive power controller is implemented on the distributed hydrogen generation scheme. The model is helpful for the assessment or optimization of both the economics and feasibility of the dedicated offshore hybrid energy farm for hydrogen production systems.