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oe1(光电查) - 科学论文

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出版时间
  • 2019
研究主题
  • Photovoltaic system
  • real-time correction term
  • Multiple Linear Regression method
  • short-term forecasting
应用领域
  • Electrical Engineering and Automation
机构单位
  • Tsinghua University
32 条数据
?? 中文（中国） ▾

  • The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

    摘要： The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.

    关键词： NARX neural network,photovoltaic (PV) power forecasting,multisite modelling,short-term forecasting,smart persistence model,classification methods

    更新于2025-09-23 15:19:57

  • Locus Coeruleus Optogenetic Light Activation Induces Long-Term Potentiation of Perforant Path Population Spike Amplitude in Rat Dentate Gyrus

    摘要： Norepinephrine (NE) long-term potentiation (NE-LTP) of the perforant path-evoked potential population spike both in vitro and in vivo. Chemical activators infused near locus coeruleus (LC), the source of DG NE, produce a NE-LTP that is associative, i.e., requires concurrent pairing with perforant path (PP) input. Here, we ask if LC optogenetic stimulation that allows us to activate only LC neurons can induce NE-LTP in DG. We use an adeno-associated viral vector containing a depolarizing channel (AAV8-Ef1a-DIO-eChR2(h134r)-EYFP-WPRE) infused stereotaxically into the LC of TH:Cre rats to produce light-sensitive LC neurons. A co-localization of ～62% in LC neurons was observed for these channels. Under urethane anesthesia, we demonstrated that 5–10 s 10 Hz trains of 30 ms light pulses in LC reliably activated neurons near an LC optoprobe. Ten minutes of the same train paired with 0.1 Hz PP electrical stimulation produced a delayed NE-LTP of population spike amplitude, but not EPSP slope. A leftward shift in the population spike input/output curve at the end of the experiment was also consistent with long-term population spike potentiation. LC neuron activity during the 10 min light train was unexpectedly transient. Increased LC neuronal ?ring was seen only for the ?rst 2 min of the light train. NE-LTP was more delayed and less robust than reported with LC chemo-activation. Previous estimates of LC axonal conduction times suggest acute release of NE occurs 40–70 ms after an LC neuron action potential. We used single LC light pulses to examine acute effects of NE release and found potentiated population spike amplitude when a light pulse in LC occurred 40–50 ms, but not 20–30 ms, prior to a PP pulse, consistent with conduction estimates. These effects of LC optogenetic activation reinforce evidence for a continuum of NE potentiation effects in DG. The single pulse effects mirror an earlier report using LC electrical stimulation. These acute effects support an attentional role of LC activation. The LTP of PP responses induced by optogenetic LC activation is consistent with the role of LC in long-term learning and memory.

    关键词： norepinephrine,perforant path,short-term potentiation,dentate gyrus,optogenetic,locus coeruleus,long-term potentiation,hippocampus

    更新于2025-09-19 17:15:36

  • [IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network

    摘要： Accurate segmentation of different brain tissue types is an important step in the study of neuronal activities using functional magnetic resonance imaging (fMRI). Traditionally, due to the low spatial resolution of fMRI data and the absence of an automated segmentation approach, human experts often resort to superimposing fMRI data on high resolution structural MRI images for analysis. The recent advent of fMRI with higher spatial resolutions offers a new possibility of differentiating brain tissues by their spatio-temporal characteristics, without relying on the structural MRI images. In this paper, we propose a patch-wise deep learning method for segmenting human brain tissues into five types, which are gray matter, white matter, blood vessel, non-brain and cerebrospinal fluid. The proposed method achieves a classification rate of 84.04% and a Dice similarity coefficient of 76.99%, which exceed those by several other methods.

    关键词： functional MRI,brain tissue segmentation,learning,long short-term memory,deep convolutional neural network

    更新于2025-09-19 17:15:36

  • [IEEE 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Harbin, China (2019.8.11-2019.8.14)] 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Power Forecasting of Photovoltaic Generation Based on Multiple Linear Regression Method with Real-time Correction Term

    摘要： This paper proposes a photovoltaic power generation forecasting model which improves Multiple Linear Regression method (MLRM) with real-time correction term traditional day-ahead, hourly power (RCT). Firstly, a generation prediction model is developed by MLRM based on qualitative variables (hour, month, weather type), quantitative variable (solar radiation intensity) and physical characteristics of interactions between the variables. Secondly, an improved is presented which adds a model named MLRM+RCT correction term based on shorter real-time measured power data to MLRM to reduce the hourly prediction errors of MLRM. MLRM+RCT is tested based on power generation data released by IEEE Energy Forecasting Group in 2014. The results show that the performance of MLRM+RCT is better than that of MLRM and a benchmark method called exponential smoothing method.

    关键词： Photovoltaic system,real-time correction term,Multiple Linear Regression method,short-term forecasting

    更新于2025-09-19 17:13:59

  • [IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Sequential Application of Static and Dynamic Mechanical Stresses for Electrical Isolation of Cell Cracks

    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2025-09-19 17:13:59

  • [IEEE 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Chengdu, China (2019.5.21-2019.5.24)] 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Short-term Photovoltaic Output Prediction Method Based on Similar Day Selection with Grey Relational Theory

    摘要： With the rapid growth of Photovoltaic (PV) power capacity, how to improve the accuracy of PV output prediction becomes an intensive problem. This paper proposes a prediction model to reduce the error of prediction by combining gray correlation theory to select similar days with BP neural network. Firstly, the factors of weather between sample days selected as similar days and forecast days are calculated to find three similar days which have the highest relevance. After that, different weights are assigned to historical data of similar days according to the degree of relevance from large to small and back propagation (BP) neural network is combined to predict the output of PV. Finally, based on the actual measurement data of a photovoltaic power station in Northeast China, this paper validates the positive effects of this model on MATLAB simulation platform. Results show that selecting similar days by grey correlation and combining BP neural network can effectively improve the prediction accuracy of short-term PV output.

    关键词： Similar day,Grey correlation theory,BP neural network,Historical data,Short-term PV output

    更新于2025-09-16 10:30:52

  • Photovoltaic power forecasting based LSTM-Convolutional Network

    摘要： The volatile and intermittent nature of solar energy itself presents a significant challenge in integrating it into existing energy systems. Accurate photovoltaic power prediction plays an important role in solving this problem. With the development of deep learning, more and more scholars have applied the deep learning model to time series prediction and achieved very good results. In this paper, a hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction. In the proposed hybrid prediction model, the temporal features of the data are extracted first by the long-short term memory network, and then the spatial features of the data are extracted by the convolutional neural network model. In order to show the superior performance of the proposed hybrid prediction model, the prediction results of the hybrid model are compared with those of the single model (long-short term memory network, convolutional neural network) and the hybrid network (Convolutional-LSTM Network) model, and the results of eight error evaluation indexes are given. The results show that the hybrid prediction model has better prediction effect than the single prediction model, and the proposed hybrid model (extract the temporal characteristics of data first, and then extract the spatial characteristics of data) is better than Convolutional-LSTM Network (extract the spatial characteristics of data first, and then extract the temporal characteristics of data).

    关键词： Convolutional-LSTM network,LSTM-Convolutional network,Photovoltaic power forecasting,Convolutional neural network,Deep learning,Long-short term memory

    更新于2025-09-16 10:30:52

  • [IEEE 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) - Fukuoka, Japan (2019.7.7-2019.7.11)] 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) - How to Establish a Sustainable Ecosystem for Photonic Integrated Circuits? What are Major Hurdles to Overcome?

    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2025-09-16 10:30:52

  • Towards automated in vivo bladder tumor stratifcation using Confocal Laser Endomicroscopy

    摘要： Purpose Urothelial carcinoma of the bladder (UCB) is the most common urinary cancer. White-light cystoscopy (WLC) forms the corner stone for the diagnosis of UCB. However, histopathological assessment is required for adjuvant treatment selection. Probe-based confocal laser endomicroscopy (pCLE) enables the visualization of the micro-architecture of bladder lesions during WLC, which allows for real-time tissue differentiation and grading of UCB. To improve the diagnostic process of UCB, computer aided classification of pCLE videos of in vivo bladder lesions were evaluated in this study. Materials and Methods We implemented pre-processing methods to optimize contrast and to reduce striping artifacts in each individual pCLE frame. Subsequently, a semi-automatic frame selection was performed. The selected frames were used to train a feature extractor, based on pre-trained ImageNet networks. A recurrent neural network, in specific long-short term memory (LSTM), was used to predict the grade of the bladder lesions. The differentiation of lesions was performed at two levels, namely (i) healthy and benign versus malignant tissue and (ii) low-grade versus high-grade papillary UCB. A total of 53 patients with 72 lesions were included in this study, resulting in approximately 140.000 pCLE frames. Results The semi-automated frame selection reduced the number of frames to approximately 66.500 informative frames. The accuracy for the differentiation of (i) healthy and benign versus malignant urothelium was 79% and (ii) high-grade and low-grade papillary UCB with 82%. Conclusions A feature extractor in combination with a LSTM results in an proper stratification of pCLE videos of in vivo bladder lesions.

    关键词： confocal laser endomicroscopy,deep learning,bladder tumor,long-short term memory,classification

    更新于2025-09-12 10:27:22

  • [IEEE 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Amsterdam, Netherlands (2019.9.24-2019.9.26)] 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - A Pixel Level Scaled Fusion Model to Provide High Spatial-Spectral Resolution for Satellite Images Using LSTM Networks

    摘要： Pixel-level fusion of satellite images coming from multiple sensors allows for an improvement in the quality of the acquired data both spatially and spectrally. In particular, multispectral and hyperspectral images have been fused to generate images with a high spatial and spectral resolution. In literature, there are several approaches for this task, nonetheless, those techniques still present a loss of relevant spatial information during the fusion process. This work presents a multi scale deep learning model to fuse multispectral and hyperspectral data, each with high-spatial-and-low-spectral resolution (HSaLS) and low-spatial-and-high-spectral resolution (LSaHS) respectively. As a result of the fusion scheme, a high-spatial-and-spectral resolution image (HSaHS) can be obtained. In order of accomplishing this result, we have developed a new scalable high spatial resolution process in which the model learns how to transition from low spatial resolution to an intermediate spatial resolution level and finally to the high spatial-spectral resolution image. This step-by-step process reduces significantly the loss of spatial information. The results of our approach show better performance in terms of both the structural similarity index and the signal to noise ratio.

    关键词： hyperspectral image,Super resolution,Data Fusion,Long Short Term Memory,Pixel level,multispectral image

    更新于2025-09-12 10:27:22