- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE Congress on Evolutionary Computation (CEC) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 IEEE Congress on Evolutionary Computation (CEC) - Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models
摘要： Development and improvement of solar forecasting models have been extensively addressed in the past years due to the importance of solar energy as a renewable energy source. This work presents an application and improvement of intra-day solar predictive models based on genetic programming. Forecasts were evaluated in time horizons of 10 minutes up to 180 minutes ahead as future steps at two completely different locations: one in northern hemisphere and another in the southern hemisphere. The improvement strategy was validated in comparison of error metrics to the ones obtained by benchmark methods of solar forecasting. The proposed model results will be presented and validated for each considered location.
关键词： solar forecasting,short-term forecasting,multigene genetic programming,intra-day forecasting
更新于2025-09-23 15:22:29
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[Institution of Engineering and Technology 12th European Conference on Antennas and Propagation (EuCAP 2018) - London, UK (9-13 April 2018)] 12th European Conference on Antennas and Propagation (EuCAP 2018) - Remote Vital Sign Recognition through Machine Learning augmented UWB
摘要： This paper describes an experimental demonstration of machine learning (ML) techniques supplementing radar to distinguish and detect vital signs of users in a domestic environment. This work augments an intelligent location awareness system previously proposed by the authors. That research employed Ultra-Wide Band (UWB) radar complemented by supervised machine learning techniques to remotely identify a person’s room location via ?oor plan training and time stamp correlations. Here, the remote breathing and heartbeat signals are analyzed through Short Term Fourier Transformation (STFT) to determine the Micro-Doppler signature of those vital signs in different room locations. Then, Multi-Class Support Vector Machine (MC-SVM) is implemented to train the system to intelligently distinguish between vital signs during different activities. Statistical analysis of the experimental results supports the proposed algorithm. This work could be used to further understand, for example, how active older people are by engaging in typical domestic activities.
关键词： Short Term Fourier Transform (STFT),Breathing,Ultra-Wide Band (UWB),Multi-Class Support Vector Machine (MC-SVM),Heartbeat,Indoor Positioning System (IPS)
更新于2025-09-23 15:22:29
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Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks
摘要： As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long–short-term memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.
关键词： convolutional neural networks,deep learning,long-short term memory,Solar power forecasting
更新于2025-09-23 15:22:29
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A Short Term Day-Ahead Solar Radiation Prediction Using Machine Learning Techniques
摘要： The task of solar power forecasting becomes vital to ensure grid constancy and to enable an optimal unit commitment and cost-effective dispatch. Each year latest techniques and approaches appear to increase the exactitude of models with the important goal of reducing uncertainty in the predictions. The aim of the paper is to compile a big part of the knowledge about solar power forcing, to focus on the most recent advancements and future trends. Firstly, the inspiration to achieve an accurate forecast is presented with the analysis of the economic implications it may have. To address the problem superlative prediction models are rummaged by us using machine learning techniques. We make a comparison between multiple regression techniques for creating prediction models, along with linear least squares and support vector machines using multiple kernel functions. Predictions are analyzed by us in our experiments for the day ahead solar radiation data and it is shown that a machine learning approach yields feasible results for short-term solar prediction. The proposed model achieves a root mean square error improvement of around 29% compared to others proposed model except one.
关键词： Forecasting,SVR,Renewable energy,Short-term,Machine learning
更新于2025-09-23 15:22:29
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Truly Concomitant and Independently Expressed Short- and Long-Term Plasticity in a Bi <sub/>2</sub> O <sub/>2</sub> Se-Based Three-Terminal Memristor
摘要： Concomitance of diverse synaptic plasticity across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable of emulating short-term (STP) and long-term plasticity (LTP) concomitantly are essential. In existing memristors, however, STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, the first demonstration of truly concomitant STP and LTP is reported in a three-terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi2O2Se is used for memristors for the first time, opening up the prospects for ultrathin, high-speed, and low-power neuromorphic devices. The concerted action of STP and LTP allows full-range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A heuristic recurrent neural circuitry model is developed to simulate the intricate “sleep–wake cycle autoregulation” process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the development of generic memristor platforms for highly dynamic neuromorphic computing.
关键词： Bi2O2Se,hybrid density functional calculations,long-term plasticity,short-term plasticity,three-terminal memristors
更新于2025-09-23 15:21:01
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[IEEE 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Busan, Korea (South) (2020.2.19-2020.2.22)] 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Transfer Learning for Photovoltaic Power Forecasting with Long Short-Term Memory Neural Network
摘要： Data-driven modeling is one of the research hotspots of photovoltaic (PV) power prediction. However, for some newly built PV power plants, there are not enough historical data to train an accurate model. Therefore, constructing a forecasting model for the PV plants lacking historical data is an urgent problem to be solved. In this paper, we propose a method to transfer the knowledge obtained from historical solar irradiance data to the output prediction. Firstly, the based on hyperparameters of the long short-term memory neural network (LSTM) are optimized and the weights in the neurons are pre-trained, then fine-tuning the deep transfer model with PV output data. In this way, knowledge can be transferred to PV output data. The from solar experimental results show that the proposed method can significantly reduce the prediction error.
关键词： Long short-term memory,Transfer learning,Photovoltaic power forecasting,Hyperparameter optimization,Data mining
更新于2025-09-23 15:21:01
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An On-Line Low-Cost Irradiance Monitoring Network with Sub-Second Sampling Adapted to Small-Scale PV Systems
摘要： Very short-term solar forecasts are gaining interest for their application on real-time control of photovoltaic systems. These forecasts are intimately related to the cloud motion that produce variations of the irradiance field on scales of seconds and meters, thus particularly impacting in small photovoltaic systems. Very short-term forecast models must be supported by updated information of the local irradiance field, and solar sensor networks are positioning as the more direct way to obtain these data. The development of solar sensor networks adapted to small-scale systems as microgrids is subject to specific requirements: high updating frequency, high density of measurement points and low investment. This paper proposes a wireless sensor network able to provide snapshots of the irradiance field with an updating frequency of 2 Hz. The network comprised 16 motes regularly distributed over an area of 15 m × 15 m (4 motes × 4 motes, minimum intersensor distance of 5 m). The irradiance values were estimated from illuminance measurements acquired by lux-meters in the network motes. The estimated irradiances were validated with measurements of a secondary standard pyranometer obtaining a mean absolute error of 24.4 W/m2 and a standard deviation of 36.1 W/m2. The network was able to capture the cloud motion and the main features of the irradiance field even with the reduced dimensions of the monitoring area. These results and the low-cost of the measurement devices indicate that this concept of solar sensor networks would be appropriate not only for photovoltaic plants in the range of MW, but also for smaller systems such as the ones installed in microgrids.
关键词： pyranometer,irradiance monitoring network,very short-term solar forecasting,microgrids,cloud enhancement,wireless sensor network,lux-meter
更新于2025-09-23 15:21:01
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Short-term Voltage Stability Enhancement in Residential Grid with High Penetration of Rooftop PV units
摘要： Short-term voltage instability (STVI) imposes a severe threat to modern distribution networks (DNs) where a large number of intermittent distributed generator (DG) units, like rooftop photovoltaic (PV), is being integrated. Consequently, most of the international standards have been revised by incorporating the requirement of dynamic voltage support (DVS) through DG units, which is a promising approach to alleviate the STVI. In this paper, a novel DVS strategy is proposed to improve the short-term voltage stability (STVS) in residential grids. In comparison with other DVS strategies, the proposed DVS scheme maximizes the active power support from PV units following a contingency utilizing maximum allowable current of the PV inverters. Moreover, the inverter design margin is taken into account in designing the proposed scheme to limit the injected grid current within maximum allowable inverter current. The impact of inverter design margin on the STVS is explained, and the effectiveness of the proposed strategy compared with conventional DVS is demonstrated. The feasibility of the DVS control strategies in practical application is studied. Several case studies are carried out on benchmark IEEE 4 bus and IEEE 13 node test feeder systems, and finally on a ring-type DN. The results show that the proposed DVS scheme is feasible, and achieved superior performance compared to the other strategies. Furthermore, it has been shown that implementation of the proposed DVS scheme can avoid the installation of an expensive 1200 kVA D-STATCOM for STVS improvement in the target system.
关键词： Distributed generators,fault induced delayed voltage recovery,low voltage ride-through,dynamic voltage support,short-term voltage stability,distribution system
更新于2025-09-23 15:21:01
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Micro-cracks detection of solar cells surface via combining short-term and long-term deep features
摘要： The machine vision based methods for micro-cracks detection of solar cells surface have become one of the main research directions with its efficiency and convenience. The existed methods are roughly classified into two categories: current viewing information based methods, prior knowledge based methods, however, the former usually adopt hand-designed features with poor generality and lacks the guidance of prior knowledge, the latter are usually implemented through the machine learning, and the generalization ability is also limited since the large-scale annotation dataset is scarce. To resolve above problems, a novel micro-cracks detection method via combining short-term and long-term deep features is proposed in this paper. The short-term deep features which represent the current viewing information are learned from the input image itself through stacked denoising auto encoder (SDAE), the long-term deep features which represent the prior knowledge are learned from a large number of natural scene images that people often see through convolutional neural networks (CNNs). The subjective and objective evaluations demonstrate that: 1) the performance of combing the short-term and long-term deep features is better than any of them alone, 2) the performance of proposed method is superior to the shallow learning based methods, 3) the proposed method can effectively detect various kinds of micro-cracks.
关键词： solar cell,stacked denoising auto encoder,long-term,convolutional neural networks,short-term,micro-cracks detection
更新于2025-09-23 15:19:57
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[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