- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions
摘要： It is important to investigate the long‐term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k‐nearest neighbor, discriminant analysis, Na?ve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single‐diode and double‐diode equivalent‐circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.
关键词： prediction of performances,PV modules modeling,classification algorithms,equivalent‐circuit models,machine learning
更新于2025-09-23 15:19:57
-
[IEEE 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Singapore, Singapore (2019.8.28-2019.8.30)] 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Near Infrared Absorption Enhancement of Graphene for High-Responsivity Photodetection
摘要： As a key component in the wind turbine system, the power electronic converter and its power semiconductors suffer from complicated power loadings related to environment, and are proven to have high failure rates. Therefore, correct lifetime estimation of wind power converter is crucial for the reliability improvement and also for cost reduction of wind power technology. Unfortunately, the existing lifetime estimation methods for the power electronic converter are not yet suitable in the wind power application, because the comprehensive mission profiles are not well specified and included. Consequently, a relative more advanced approach is proposed in this paper, which is based on the loading and strength analysis of devices and takes into account different time constants of the thermal behaviors in power converter. With the established methods for loading and lifetime estimation for power devices, more detailed information of the lifetime-related performance in wind power converter can be obtained. Some experimental results are also included to validate the thermal behavior of power device under different mission profiles.
关键词： lifetime prediction,IGBT,power semiconductor device,thermal cycling,wind power,mission profiles
更新于2025-09-23 15:19:57
-
[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Sputtered Aluminum Oxide and p <sup>+</sup> Amorphous Silicon Back-Contact for Improved Hole Extraction in Polycrystalline CdSe <sub/>x</sub> Te <sub/>1-x</sub> and CdTe Photovoltaics
摘要： Previous studies have attempted to investigate the peripheral neural mechanisms implicated in tactile perception, but the neurophysiological data in humans involved in tactile spatial location perception to help the brain orient the body and interact with its surroundings are not well understood. In this paper, we use single-trial electroencephalogram (EEG) measurements to explore the perception of tactile stimuli located on participants’ right forearm, which were approximately equally spaced centered on the body midline, 2 leftward and 2 rightward of midline. An EEG-based signal analysis approach to predict the location of the tactile stimuli is proposed. Offline classification suggests that tactile location can be detected from EEG signals in single trial (four-class classifier for location discriminate can achieve up to 96.76%) with a short response time (600 milliseconds after stimulus presentation). From a human-machine-interaction (HMI) point of view, this could be used to design a real-time reactive control machine for patients, e.g., suffering from hypoesthesia.
关键词： Electroencephalogram (EEG),prediction,tactile,spatial location perception
更新于2025-09-23 15:19:57
-
Coexistence of quasi-CW and SBS-boosted self-Q-switched pulsing in ytterbium-doped fiber laser with low Q-factor cavity
摘要： As a key component in the wind turbine system, the power electronic converter and its power semiconductors suffer from complicated power loadings related to environment, and are proven to have high failure rates. Therefore, correct lifetime estimation of wind power converter is crucial for the reliability improvement and also for cost reduction of wind power technology. Unfortunately, the existing lifetime estimation methods for the power electronic converter are not yet suitable in the wind power application, because the comprehensive mission profiles are not well specified and included. Consequently, a relative more advanced approach is proposed in this paper, which is based on the loading and strength analysis of devices and takes into account different time constants of the thermal behaviors in power converter. With the established methods for loading and lifetime estimation for power devices, more detailed information of the lifetime-related performance in wind power converter can be obtained. Some experimental results are also included to validate the thermal behavior of power device under different mission profiles.
关键词： lifetime prediction,IGBT,power semiconductor device,thermal cycling,wind power,mission profiles
更新于2025-09-23 15:19:57
-
[IEEE 2019 Days on Diffraction (DD) - St. Petersburg, Russia (2019.6.3-2019.6.7)] 2019 Days on Diffraction (DD) - Mathematical modeling of pulsed laser therapy
摘要： Predicting the popularity of online videos is an important task for the service design, advertisement placement, network management, and so on. In this paper, we tackle the challenge head-on by casting the popularity prediction problem into two consecutive tasks: online video future popularity level prediction and online video future view count prediction. We first predict the future popularity levels of online videos, based on a rich set of features and effective classification technique. Then, according to the popularity level transitions, we build specialized regression models to predict the future view count values. We validate our approach on the exhaustive dataset of a leading online video service provider in China, namely, Youku. The experimental results show that comparing with two state-of-the-art baseline models, our proposed method can significantly decrease the relative prediction errors of 32.25% and 19.82%, respectively. At last, we also discuss the model setup and feature importance of our method. We believe our work can provide direct help in practical for the interested parties of online video service, such as service providers, online advisers, and network operators.
关键词： video popularity prediction,Online video service
更新于2025-09-23 15:19:57
-
[IEEE 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) - Mangalore, India (2018.9.6-2018.9.8)] 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) - A 50?? CPW-FED Rhombus Shaped Patch Antenna Using Rightangled Isosceles Triangle Fractal
摘要： Short-term traf?c prediction plays a critical role in many important applications of intelligent transportation systems such as traf?c congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traf?c data. In this paper, we present a novel short-term traf?c ?ow prediction approach based on dynamic tensor completion (DTC), in which the traf?c data are represented as a dynamic tensor pattern, which is able capture more information of traf?c ?ow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traf?c ?ow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the ef?cacy of the proposed approach is validated on the experiments of traf?c ?ow prediction, particularly when dealing with incomplete traf?c data.
关键词： missing data,dynamic tensor completion,Short-term traf?c ?ow prediction,multi-mode information
更新于2025-09-23 15:19:57
-
Predicting Device Parameters for Dye-Sensitized Solar Cells from Electronic Structure Calculations to Reproduce Experiment
摘要： Given that improvements to power conversion efficiency (PCE) of dye-sensitized solar cells (DSSCs) have slowed in recent years, a means to accurately predict device parameters yielded by trial dyes in silico, without having to synthesize them, would be extremely valuable to speed up the design process. Currently, the best performing methods of calculating device parameters rely on a set of experimentally determined kinetic coefficients. In practice, it is very difficult to measure these kinetic parameters accurately, limiting the overall accuracy of such predictive methods. This work proposes a model to obtain key parameters such as JSC, VOC and PCE using only results from density functional theory (DFT) and time-dependent DFT calculations, noting that rates of electron transfer steps are ultimately linked to the electronic structure of the dye…TiO2 working electrode. Six organic DSSC dyes from dissimilar chemical classes (L0, L1, L2, WS-2, WS-92 and C281) were chosen to demonstrate the power of this approach. Their a priori known experimentally determined device performance metrics served to validate our predictions. The greatest absolute error in our predicted PCE values was 0.36% relative to experiment, whilst the greatest fractional error was 0.042. This indicates the proposed model offers a dramatic improvement on previous predictive methods for DSSC device parameters, both in accuracy and consistency. Moreover, the spirit of designing such a predictive model has great potential to be applied to other photovoltaic applications, further enabling the design of novel, highly efficient photoactive materials.
关键词： performance prediction,energy-conversion efficiency,dye-sensitized solar cells,photovoltaic properties,density functional theory
更新于2025-09-23 15:19:57
-
Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding
摘要： Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.
关键词： deep neural network,camera calibration,laser vision sensor,gas metal arc welding,weld quality prediction
更新于2025-09-23 15:19:57
-
Linear-Gompertz Model-Based Regression of Photovoltaic Power Generation by Satellite Imagery-Based Solar Irradiance
摘要： A simple yet accurate photovoltaic (PV) performance curve as a function of satellite-based solar irradiation is necessary to develop a PV power forecasting model that can cover all of South Korea, where more than 35,000 PV power plants are currently in operation. In order to express the nonlinear power output of the PV module with respect to the hourly global horizontal irradiance derived from satellite images, this study employed the Gompertz model, which is composed of three parameters and the sigmoid equation. The nonphysical behavior of the Gompertz model within the low solar irradiation range was corrected by combining a linear equation with the same gradient at the conjoint point. The overall ?tness of Linear-Gompertz regression to the 242 PV power plants representing the country was R2 = 0.85 and nRMSE = 0.09. The Gompertz model coe?cients showed normal distributions and equivariance of standard deviations of less than 15% by year and by season. Therefore, it can be conjectured that the Linear-Gompertz model represents the whole country’s PV system performance curve. In addition, the Gompertz coe?cient C, which controls the growth rate of the curve, showed a strong correlation with the capacity factor, such that the regression equation for the capacity factor could be derived as a function of the three Gompertz model coe?cients with a ?tness of R2 = 0.88.
关键词： photovoltaic system performance,Gompertz model,power output prediction,satellite-derived global horizontal irradiance,numerical analysis
更新于2025-09-23 15:19:57
-
[IEEE 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - New Delhi, India (2019.11.16-2019.11.17)] 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - Photovoltaic Based Application for Domestic Load
摘要： In this paper, we proposed a decentralized cooperative lane-changing decision-making framework for connected autonomous vehicles, which is composed of three modules, i.e., state prediction, candidate decision generation, and coordination. In other words, each connected autonomous vehicle makes cooperative lane-changing decision independently. In the state prediction module, we employed existing cooperative car-following models to predict the vehicles’ future state. In the candidate decision generation module, we proposed incentive based model to generate a candidate decision. In the candidate decision coordination module, we proposed an algorithm to avoid candidate lane-changing decision that may lead to a vehicle collision or traf?c deterioration to be ?nal decision. Moreover, the effects of decentralized cooperative lane-changing decision-making framework on traf?c stability, ef?ciency, homogeneity, and safety are investigated in a numerical simulation experiment. Some stability, ef?ciency, homogeneity, and safety indicators are evaluated and show the high potential of our proposed framework in traf?c dynamics.
关键词： candidate decision coordination module,state prediction module,decentralized cooperative lane-changing decision-making framework,candidate decision generation module,Connected autonomous vehicles
更新于2025-09-23 15:19:57