Faced with the large number of images of electrical equipment in electrical drone inspections, the efficiency of human visual recognition is low, and it is difficult to locate high-temperature defects in electrical equipment quickly. Therefore, this paper proposes a method for screening images of high-temperature defects in electrical equipment. Firstly, a target dataset is constructed, and data augmentation is applied to address the issue of imbalanced target samples. Then, improvements are made to the YOLOv5 network by adding a object detection layer to obtain larger feature maps. Finally, the modified YOLOv5 network is trained and compared with methods such as YOLOv5 and YOLOv4 through experimental studies. The experimental results demonstrate that the proposed method can effectively enhance the network's detection capabilities for targets and achieve accurate identification of electrical high-temperature defects, even under conditions with significant infrared interference.
Due to the low accuracy of energy consumption prediction when the hyper-parameters of the long-term and short-term neural network model (LSTM) are experimentally determined. This paper proposes to optimize the hyper-parameters based on Bayesian algorithm and apply them to LSTM to construct a combined energy consumption prediction model. Seven strongly correlated factor data of a building are selected as input feature quantities to train the combined model. The prediction results are analyzed to verify the prediction accuracy of the model. And the ARIMA, RNN and LSTM models are compared and verified. The results show that the prediction accuracy of Bayesian optimized LSTM model reaches 97.2 %, so it has high reliability for building energy consumption prediction.
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