The Short-Term Forecasting of Asymmetry Photovoltaic Power Based on the Feature Extraction of PV Power and SVM Algorithm

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual underground working environment of the mine, a machine vision platform for coal gangue identification was built and the coal gangue image acquisition experiment was carried out. Then, the images of coal and gangue were denoised by median filtering, and the coal and gangue features were extracted by using the HOG combined with LBP feature extraction algorithm, and these features were normalized and principal component analysis (PCA) reduced dimension to remove the correlation and redundancy between the features. Finally, SVM, SVM optimized by genetic algorithm (GA-SVM), SVM optimized by particle swarm optimization (PSO-SVM) algorithm, and SVM optimized by grey wolf optimization (GWO-SVM) algorithm were used as classifiers for identification and classification, respectively. The experimental results show that the GWO-SVM classification model has the highest accuracy, and the average classification accuracies were 96.49% and 94.82% of the training set and test set, respectively, which shows that grey wolf algorithm to optimize support vector machine has a good effect on classification of coal gangue images, which proves the feasibility and accuracy of the proposed method.

A Human Defecation Prediction Method Based on Multi-Domain Features and Improved Support Vector Machine

The difficulty of defecation seriously affects the quality of life of the bedridden elderly. To solve the problem that it is difficult to know the defecation time of the bedridden elderly, this paper proposed a human pre-defecation prediction method based on multi-domain features and improved support vector machine (SVM) using bowel sound as the original signal. The method includes three stages: multi-domain features extraction, feature optimization, and defecation prediction. In the stage of multi-domain features extraction, statistical analysis, fast Fourier transform (FFT), and wavelet packet transform are used to extract feature information in the time domain, frequency domain, and time-frequency domain. The symmetry of the bowel sound signal in the time domain, frequency domain, and time-frequency domain will change when the human has the urge to defecate. In the feature optimization stage, the Fisher Score (FS) algorithm is introduced to select meaningful and sensitive features according to the importance of each feature, aiming to remove redundant information and improve computational efficiency. In the stage of defecation prediction, SVM is optimized by the gray wolf optimization (GWO) algorithm to realize human defecation prediction. Finally, experimental analysis of the bowel sound data collected during the study is carried out. The experimental result shows that the proposed method could achieve an accuracy of 92.86% in defecation prediction, which proves the effectiveness of the proposed method.

A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.

Review on Performance Enhancement of Photovoltaic/Thermal–Thermoelectric Generator Systems with Nanofluid Cooling

Photovoltaics (PVs) are an effective technology to harvest the solar energy and satisfy the increasing global electricity demand. The effectiveness and life span of PVs could be enhanced by enabling effective thermal management. The conversion efficiency and surface temperature of PVs have an inverse relationship, and hence the cooling of PVs as an emerging body of work needs to have attention paid to it. The integration of a thermoelectric generator (TEG) to PVs is one of the widely applied thermal management techniques to improve the performance of PVs as well as combined systems. The TEG utilizes the waste heat of PVs and generate the additional electric power output. The nanofluid enables superior thermal properties compared to that of conventional cooling fluids, and therefore the performance of photovoltaic/thermal–thermoelectric generator (PV/T-TEG) systems with nanofluid cooling is further enhanced compared to that of conventional cooling. The TEG enables a symmetrical temperature difference with a hot side due to the heat from PVs, and a cold side due to the nanofluid cooling. Therefore, the symmetrical thermal management system, by integrating the PV/T, TEG, and nanofluid cooling, has been widely adopted in recent times. The present review comprehensively summarizes various experimental, numerical, and theoretical research works conducted on PV/T-TEG systems with nanofluid cooling. The research studies on PV/T-TEG systems with nanofluid cooling were reviewed, focusing on the time span of 2015–2021. This review elaborates the various approaches and advancement in techniques adopted to enhance the performance of PV/T-TEG systems with nanofluid cooling. The application of TEG with nanofluid cooling in the thermal management of PVs is an emerging research area; therefore, this comprehensive review can be considered as a reference for future development and innovations.