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In this paper, a tracking algorithm based on the residual neural network model and machine learning is proposed. Compared with the widely used VGG network, the residual neural network has deeper characteristic layers and special additional layer structure, which break the symmetry of the network and reduce the degradation of the neural network. The additional layer and convolution layer are used for feature fusion to represent the target. The multi-features of the object can be captured by using the developed algorithm, so that the accuracy of tracking can be improved in some complex scenarios. In addition, we defined a new measure to calculate the similarity of different image regions and find the optimal matched region. The search area is delimited according to the continuity of the target motion, which improves the real-time performance of tracking. The experimental results illustrate that the proposed algorithm achieved a higher accuracy while taking into account the real time performance, especially in dealing with some complex scenarios such as deformation, rotation changes and background clutters, in comparison with the Multi-Domain Network (MDNet) algorithm based on a convolutional neural network. [ABSTRACT FROM AUTHOR]

Forecasting has always been the backbone of planning studies in the power system. With the advent of the restructured power system and also the integration of renewable energy sources, it has become more challenging. Accurate load forecasting leads to a reduction in supply–demand mismatch, whereas solar insolation/wind speed forecasting improves generation side planning. With the rapid growth in computer-based intelligence, self-learning mechanisms together with continuously growing volume of the dataset and affordable cost-effective data storage options, deep learning performance keeps getting better and has made a huge impact to solve the complex problems associated with the power system network. In this paper, we propose a classical long short-term memory neural network model for a combined day-ahead load and solar insolation forecasting of residential consumers equipped with rooftop PV based on weather classification. The effectiveness of the proposed model based on mean absolute error, mean bias error and coefficient of correlation (R-Squared) is compared with that of the standard artificial neural network and convolutional neural network models. [ABSTRACT FROM AUTHOR]

• Saffron adulterants detected and classified using a deep learning algorithm. • RGB photos took under uncontrolled/unstructured conditions. • Five CNN models were proposed for classification. • The best-proposed model can process an image in 36.26 ms with 99.67% accuracy. Saffron is the most expensive spice in the world which is traded internationally. Hence, Saffron adulterants is always a challenging issue. It should be noted that traditional methods that are being used to solve this problem, are costly and time-consuming. In this study, for the first time, Saffron adulterants was detected using a deep neural network and RGB photos taken under uncontrolled/unstructured conditions. There were five classes of fake Saffron include mixed saffron stamen and dyed straw, mixed stigma and stamens, dyed orange blossom, Safflower and dyed stamens, and two classes of genuine Saffron consisting of freeze-drying and microwave-drying saffron used to design, develop and optimize a Convolutional Neural Network (CNN). 60% of the images were used to train and optimize the neural network model. For preventing over-training, the number of images used in the training phase increased by applying the augmentation method. The general structure of the model to detect saffron adulterants consisted of multiple convolutional (input layer), pooling layers and fully-connected layers, respectively. In this study, five structures (S1, S2, S3, S4 and S5) were proposed for CNN using Batch Normalization methods. Additionally, this paper compared these five structures with Popular Convolutional Networks consisted of VGG11, ResNet18, ResNet50, Inception V3 and DarkNet53. According to the results, the best configuration was S5 that achieved the accuracy of 99.8% and the cross-entropy loss of 0.019. Although the time process of this structure was 36.26 ms, the shortest time process was 19.38 ms related to the S1. This number was more than 300 ms for Popular CNN. Based on the results of this study, imaging without restrictions with CNN can detect Saffron adulterants. [ABSTRACT FROM AUTHOR]

The purpose of feeder-level energy disaggregation is to decouple the net load measured at the feeder-head into various components. This technology is vital for power system utilities since increased visibility of controllable loads enables the realization of demand-side management strategies. However, energy disaggregation at the feeder level is difficult to realize since the high penetration of embedded generation masks the actual demand and different loads are highly aggregated. In this paper, the solar energy at the grid supply point is separated from the net load at first via either an unsupervised upscaling method or the supervised gradient boosting regression tree (GBRT) method. To deal with the uncertainty of the load components, the probabilistic energy disaggregation models based on multi-quantile recurrent neural network model (multi-quantile long short-term memory (MQ-LSTM) model and multi-quantile gated recurrent unit (MQ-GRU) model) are proposed to disaggregate the demand load into thermostatically controlled loads (TCLs), non-thermostatically controlled loads (non-TCLs), and non-controllable loads. A variety of relevant information, including feeder measurements, meteorological measurements, calendar information, is adopted as the input features of the model. Instead of providing point prediction, the probabilistic model estimates the conditional quantiles and provides prediction intervals. A comprehensive case study is implemented to compare the proposed model with other state-of-the-art models (multi-quantile convolutional neural network (MQ-CNN), quantile gradient boosting regression tree (Q-GBRT), Quantile Light gradient boosting machine (Q-LGB)) from training time, reliability, sharpness, and overall performance aspects. The result shows that the MQ-LSTM can estimate reliable and sharp Prediction Intervals for target load components. And it shows the best performance among all algorithms with the shortest training time. Finally, a transfer learning algorithm is proposed to overcome the difficulty to obtain enough training data, and the model is pre-trained via synthetic data generated from a public database and then tested on the local dataset. The result confirms that the proposed energy disaggregation model is transferable and can be applied to other feeders easily. • Feeder level energy disaggregation system to separate demand into sub-components. • A multi-quantile LSTM model that estimates the conditional quantiles. • A solar energy separation model to detect the behind-the-meter PV generation. • A transfer learning model to apply a pre-trained model to other location. [ABSTRACT FROM AUTHOR]