Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism

Computers in Human Behavior, Год журнала: 2023, Номер 154, С. 108097 - 108097

Опубликована: Дек. 27, 2023

Язык: Английский

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Output power prediction of stratospheric airship solar array based on surrogate model under global wind field

Chinese Journal of Aeronautics, Год журнала: 2024, Номер 38(4), С. 103244 - 103244

Опубликована: Сен. 18, 2024

Язык: Английский

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Implementation of high step-up power converter for fuel cell application with hybrid MPPT controller

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 9, 2024

Abstract As of now, there are multiple types renewable energy sources available in nature which hydro, wind, tidal, and solar. Among all that the solar source is used many applications because its features low maitainence cost, less human power for handling, a clean source, more availability nature, reduced carbon emissions. However, disadvantages networks continuously depending on weather conditions, high complexity storage, lots installation place required. So, this work, Proton Exchange Membrane Fuel Stack (PEMFS) utilized supplying to local consumers. The merits fuel stack density, ability work at very temperature values, efficient heat maintenance, water management. Also, gives quick startup response. only demerit PEMFS excessive current production, plus output voltage. To optimize supply stack, Wide Input Operation Single Switch Boost Converter (WIOSSBC) circuit placed across improve load voltage profile. advantages WIOSSBC ripples, uniform supply, good conversion ratio. Another issue nonlinear production. linearize Grey Wolf Algorithm Dependent Fuzzy Logic Methodology (GWADFLM) introduced article maintaining operating point cell near Maximum Power Point (MPP) place. entire system investigated by utilizing MATLAB software.

Язык: Английский

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Study on Integrated Neural Networks and Fuzzy Logic Control for Autonomous Electric Vehicles

Advances in web technologies and engineering book series, Год журнала: 2024, Номер unknown, С. 104 - 127

Опубликована: Май 16, 2024

This chapter presents a comprehensive study on the integration of neural networks and fuzzy logic control techniques for enhancing autonomy electric vehicles (EVs). The these two paradigms aims to overcome limitations traditional approaches by leveraging complementary strengths in learning complex patterns handling uncertainty imprecision. discusses design, implementation, evaluation an autonomous EV system that utilizes vehicle dynamics decision-making various driving scenarios. Through extensive simulations experiments, effectiveness robustness proposed integrated approach are demonstrated, showcasing its potential improving safety, efficiency, adaptability EVs real-world environments.

Язык: Английский

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Identification of a different design of a photovoltaic thermal collector based on fuzzy logic control and the ARMAX model

Thermal Science and Engineering Progress, Год журнала: 2024, Номер 48, С. 102395 - 102395

Опубликована: Янв. 11, 2024

Язык: Английский

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Binary firefly algorithm based reconfiguration for maximum power extraction under partial shading and machine learning approach for fault detection in solar PV arrays

Applied Soft Computing, Год журнала: 2024, Номер 154, С. 111318 - 111318

Опубликована: Фев. 2, 2024

Язык: Английский

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Solar irradiance forecasting based on direct explainable neural network

Energy Conversion and Management, Год журнала: 2020, Номер 226, С. 113487 - 113487

Опубликована: Окт. 12, 2020

Язык: Английский

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Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods

Energy Reports, Год журнала: 2021, Номер 7, С. 7601 - 7614

Опубликована: Ноя. 1, 2021

As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing forecasting choosing right inputs to achieve most accurate prediction.Weather datasets, past power data sets, or both sets can be utilized build different models.However, operators grid-connected PV farms do not always have full available them especially over an extended period time required techniques multiple regression (MR) artificial neural network (ANN).Therefore, research reported here considered these two main approaches building prediction models compared their performance when utilizing structural, time-series, hybrid methods for input.Three years generation (of actual farm) well historical same location) with several were collected test six models.Models built designed forecast a 24-hour ahead horizon 15 min resolutions.Results comparative analysis show that accuracy depending on input method used model: ANN perform better than MR regardless used.The results in techniques, while using time-series least models.Furthermore, sensitivity shows poor quality does impact negatively structural approach.

Язык: Английский

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Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 112, С. 104860 - 104860

Опубликована: Апрель 13, 2022

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, CNN algorithm is used to extract local patterns as well common features that occur recurrently in time series data at different intervals. Then, SVR subsequently adopted replace fully connected layers predict daily GSR six solar farms Queensland, Australia. To develop CSVR we adopt most pertinent meteorological variables from Climate Model and Scientific Information Landowners database. From pool of Models ground-based observations, optimal are selected through metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters proposed optimized mean HyperOpt method, overall performance objective benchmarked against eight alternative DL methods, some other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML M5TREE) methods. results obtained shows model can offer several predictive advantages over models, conventional ML models. Specifically, note recorded root square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 m2 tested compared 2.514–3.879 m2/1.939–2.866 algorithms. Consistent this predicted error, correlation measured GSR, including Willmott's, Nash-Sutcliffe's coefficient Legates & McCabe's Index was relatively higher methods all sites. Accordingly, advocates merits provide viable accurately renewable energy exploitation, demand or forecasting-based applications.

Язык: Английский

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Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

IEEE Access, Год журнала: 2022, Номер 10, С. 13852 - 13869

Опубликована: Янв. 1, 2022

Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks fire photovoltaic (PV) systems. However, issues of interest for PV systems remain an open-ended challenge due to manual time-consuming processes that require relevant domain knowledge experience diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures novel DL approaches achieve real-time automatic system. research employed wavelet packet transform (WPT) data preprocessing technique handle voltage signal collected feeding them inputs consist equilibrium optimizer algorithm (EOA) long short-term memory (LSTM-SAE) approaches. The are able extract features automatically from preprocessed without requiring any previous knowledge, therefore can override traditional shortages feature extraction selection optimal extracted features. These desirable anticipated speed up capability proposed model with higher accuracy. In order determine performance model, we carried out comprehensive evaluation study on 250-kW grid-connected this paper, symmetrical asymmetrical faults have been studied involving all phases ground single phase ground, phase, three-phase ground. simulation results validate efficacy terms computation time, accuracy detection, noise robustness. Comprehensive comparisons between studies demonstrate multidisciplinary applications present study.

Язык: Английский

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