- 标题
- 摘要
- 关键词
- 实验方案
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Application of Generalized Regression Neural Network in Predicting the Performance of Solar Photovoltaic Thermal Water Collector
摘要： Solar photovoltaic thermal water collector (SPV/T-WC) is a hybrid device which converts power from the solar energy in to thermal and electrical simultaneously. The performance of such SPV/T-WC mainly depends on its electrical and thermal power output. Besides the performance of SPV/T-WC, is more sensitive to the transient nature of electrical and thermal power output. Thus a demand for predicting the performance variations in the SPV/T-WC is demand by users. Only limited performance prediction based research works are attempted in the performance prediction of the SPV/T-WC either numerically or by using cognitive models. In this study, two generalized regression neural network (GRNN) models are proposed to predict the transient performance variations in the SPV/T-WC. The two individual objectives of the ?rst and second model include the prediction of overall power output and the overall ef?ciency delivered by an SPV/T-WC system. Both the GRNN models proposed in this study consist of two inputs and single output. In order to train this GRNN model, real time experiments are conducted with stand-alone SPV/T-WC for four continuous days. Then based on such experimental data sets, GRNN models are trained, tested, and validated. The results predicted by the both GRNN models are in good agreement with the real time experimental results. The overall accuracy of the proposed GRNN models in predicting the performance is 95.36% and 96.22% respectively.
关键词： Solar,Water,Collector,Thermal,accuracy,Photovoltaic,GRNN,Prediction
更新于2025-09-23 15:21:01
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Snow Loss Prediction for Photovoltaic Farms Using Computational Intelligence Techniques
摘要： With the recent widespread deployment of Photovoltaic (PV) panels in the northern snow-prone areas, performance analysis of these panels is getting much more importance. Partial or full reduction in energy yield due to snow accumulation on the surface of PV panels, which is referred to as snow loss, reduces their operational efficiency. Developing intelligent algorithms to accurately predict the future snow loss of PV farms is addressed in this article for the first time. The article proposes daily snow loss prediction models using machine learning algorithms solely based on meteorological data. The algorithms include regression trees, gradient boosted trees, random forest, feed-forward and recurrent artificial neural networks, and support vector machines. The prediction models are built based on the snow loss of a PV farm located in Ontario, Canada which is calculated using a 3-stage model and hourly data records over a 4-year period. The stages of the aforementioned model consist of: stage I: yield determination, stage II: power loss calculation, and stage III: snow loss extraction. The functionality of the proposed prediction models is validated over the historical data and the optimal hyperparameters are selected for each model to achieve the best results. Among all the models, gradient boosted trees obtained the minimum prediction error and thus the best performance. The results achieved prove the effectiveness of the proposed models for the prediction of daily snow loss of PV farms.
关键词： snow loss,Intelligent prediction,snowfall,photovoltaic (PV) farm,machine learning
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models
摘要： Spectrum sensing is used to detect spectrum holes and find active primary users while randomly selecting channel for sensing lead to secondary user’s low throughput in high traffic cognitive radio networks. Spectrum prediction forecasts future channel states on the basis of historical information. A new frame structure is proposed in this letter for the imperfect spectrum prediction, resulting to select channels for sensing only from the channels predicted to be idle. Simulation results show that secondary user’s throughput is significantly enhanced by imperfect spectrum prediction. The impacts of traffic intensity, prediction errors, and channel number on the throughput are also investigated in this study.
关键词： frame structure,Imperfect spectrum prediction,cognitive radio networks
更新于2025-09-23 15:21:01
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[IEEE 2019 14th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS) - Nis, Serbia (2019.10.23-2019.10.25)] 2019 14th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS) - A Laser Beam for Boosting the Power Added Efficiency of an X-Band GaN MMIC Amplifier
摘要： Spectrum sensing is used to detect spectrum holes and find active primary users while randomly selecting channel for sensing lead to secondary user’s low throughput in high traffic cognitive radio networks. Spectrum prediction forecasts future channel states on the basis of historical information. A new frame structure is proposed in this letter for the imperfect spectrum prediction, resulting to select channels for sensing only from the channels predicted to be idle. Simulation results show that secondary user’s throughput is significantly enhanced by imperfect spectrum prediction. The impacts of traffic intensity, prediction errors, and channel number on the throughput are also investigated in this study.
关键词： frame structure,Imperfect spectrum prediction,cognitive radio networks
更新于2025-09-23 15:21:01
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Growth of InGaAs solar cells on InP(001) miscut substrates using solid-source molecular beam epitaxy
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： feature extraction,linear prediction,Brain-computer interface,orthogonal transform,channel selection
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 8th International Conference Nanomaterials: Application & Properties (NAP) - Zatoka, Ukraine (2018.9.9-2018.9.14)] 2018 IEEE 8th International Conference Nanomaterials: Application & Properties (NAP) - Study of Self-assembled 2D Ag Nanostructures Intercalated into In <sub/>4</sub> Se <sub/>3</sub> Layered Semiconductor Crystal
摘要： This paper presents an advanced depth intra-coding approach for 3D video coding based on the High Efficiency Video Coding (HEVC) standard and the multiview video plus depth (MVD) representation. This paper is motivated by the fact that depth signals have specific characteristics that differ from those of natural signals, i.e., camera-view video. Our approach replaces conventional intra-picture coding for the depth component, targeting a consistent and efficient support of 3D video applications that utilize depth maps or polygon meshes or both, with a high depth coding efficiency in terms of minimal artifacts in rendered views and meshes with a minimal number of triangles for a given bit rate. For this purpose, we introduce intra-picture prediction modes based on geometric primitives along with a residual coding method in the spatial domain, substituting conventional intra-prediction modes and transform coding, respectively. The results show that our solution achieves the same quality of rendered or synthesized views with about the same bit rate as MVD coding with the 3D video extension of HEVC (3D-HEVC) for high-quality depth maps and with about 8% less overall bit rate as with 3D-HEVC without the combination of related depth tools. At the same time, the combination of 3D video with 3D computer graphics content is substantially simplified, as the geometry-based depth intra signals can be represented as a surface mesh with about 85% less triangles, generated directly in the decoding process as an alternative decoder output.
关键词： 3D video coding,High Efficiency Video Coding (HEVC),multiview video plus depth (MVD),depth intra coding,wedgelets,mesh extraction,inter component prediction,3D video extension of HEVC (3D-HEVC)
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - PV Plant Performance Loss Rate Assessment: Significance of Data Filtering and Aggregation
摘要： Predicting the performance of parallel scienti?c applications is becoming increasingly complex. Our goal was to characterize the behavior of message-passing applications on different target machines. To achieve this goal, we developed a method called parallel application signature for performance prediction (PAS2P), which strives to describe an application based on its behavior. Based on the application’s message-passing activity, we identi?ed and extracted representative phases, with which we created a parallel application signature that enabled us to predict the application’s performance. We experimented with using different scienti?c applications on different clusters. We were able to predict execution times with an average accuracy greater than 97 percent.
关键词： performance prediction,application signature,Parallel application
更新于2025-09-23 15:21:01
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Effect of fruit moving speed on online prediction of soluble solids content of apple using Vis/NIR diffuse transmission
摘要： The effect of fruit moving speed on online prediction of soluble solids content (SSC) of “Fuji” apples based on visible and near-infrared (Vis/NIR) spectroscopy was studied. Diffuse transmission spectra between 615 and 1,045 nm were collected with a commercial online system at speeds of 0.3 m/s (S1), 0.5 m/s (S2), and 0.7 m/s (S3). Compensation models for SSC of each speed alone (local models) and all speeds (global model) were established using partial least squares (PLS). For global model, spectra of each sample were divided into three parts (P1, P2, and P3), three kinds of spectra partition combinations (P12, P13, and P23) were established. Results showed that S3 performed better and the influence of speed on spectra greatly affected SSC evaluation accuracy between local models. Comparatively, global model was insensitive to fruit moving speed variation and effective wavelengths (EWs) selected by competitive adaptive reweighted sampling (CARS) after Savitzky–Golay smoothing (SGS) achieved better results than local models. Importantly, 36 EWs selected by CARS after SGS of global-P13 model achieved the best results with rp and RMSEP of 0.8419, 0.8895, 0.8948 and 0.6281, 0.5318, 0.5196(cid:1)Brix, respectively. Generally, global-P13 model with EWs is promisingly applied to online SSC prediction of apple by Vis/NIR diffuse transmission.
关键词： soluble solids content,online prediction,effective wavelengths,competitive adaptive reweighted sampling,partial least squares,fruit moving speed,apple,diffuse transmission,Vis/NIR spectroscopy
更新于2025-09-23 15:21:01
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Online Degradation Detection/Prediction Method for Current Transfer Ratio of Photo-Coupler Installed in Digitally-Controlled Switching Mode Power Supply
摘要： The software-implemented degradation detection/prediction of the current transfer ratio (CTR) of a photo-coupler installed in a digitally-controlled switching mode power supply was studied. The photo-coupler is one of the key devices in an isolated power supply circuit, which transmits a voltage/current signal to a controller though the isolation gap. If the CTR of the photo-coupler degrades to halfway between the normal value and the threshold of the hardware protection circuit, overvoltage/current may be supplied continuously to the load circuit and possibly cause a severe failure. By comparing the theoretical pulse width modulation (PWM) duty, which is calculated from the input/output voltage and the pre-measured power supply circuit e?ciency, and the applied PWM duty, which is calculated via feedback control, CTR degradation is detectable online. In this paper, we describe the concept of this method and verify it using both simulation and prototyping circuits.
关键词： digitally-controlled power supply,current transfer ratio,failure prediction,degradation,photo-coupler,CTR
更新于2025-09-23 15:21:01
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Assessing model performance of daily solar irradiance forecasts over Australia
摘要： In response to the rapid solar power installation worldwide, the solar industry is calling for more accurate solar irradiance forecasts with finer temporal and spatial granularity. It is timely to investigate if this need has been properly met by recent advancements in numerical weather prediction modelling. In this study, we validate and compare the current ability of three leading numerical weather prediction models to forecast daily solar irradiance for Australia. We found that all three models investigated perform well in the middle and west of Australia where clear sky weather prevails but struggle with forecasting solar irradiance in climatologically cloudy areas to some extent. In particular, the Global Forecast System (GFS) tends to significantly overpredict solar irradiance in southeastern Australia including Tasmania whilst the Australian Community Climate and Earth-System Simulator (ACCESS) system systematically underpredicts solar irradiance in northern Australia. The recent ERA5 reanalysis, which employs the Integrated Forecast System (IFS) to forecast solar irradiance, performs relatively robustly across Australia without notable deficiency, earning an overall forecast skill (defined as relative improvement in RMSE against 1-day persistence of clear-sky index) of 0.38 for 12-hour ahead forecasts of daily solar irradiance. An increase of the forecast skill to 0.44 is observed by linearly blending the three models.
关键词： Model blending,Solar forecasting,Numerical weather prediction,Forecast time horizon,Clear-sky index,Forecast skill
更新于2025-09-23 15:21:01