Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilities by formulating the generation, transmission and distribution of electric energy in advance. However, the nonlinear patterns and dynamics of load data still make accurate load forecasting a challenging task. To address this issue, a deep temporal convolutional network (TCN)-based hybrid model combined with variational mode decomposition (VMD) and self-attention mechanism (SAM) is proposed in this study. Firstly, VMD is used to decompose the original load data into a series of intrinsic mode components that are used to reconstruct a feature matrix combined with other external factors. Secondly, a three-layer convolutional neural network is used as a deep network to extract in-depth features between adjacent time points from the feature matrix, and then the output matrix captures the long-term temporal dependencies using the TCN. Thirdly, long short-term memory (LSTM) is utilized to enhance the extraction of temporal features, and the correlation weights of spatiotemporal features are future-adjusted dynamically using SAM to retain important features during the model training. Finally, the load forecasting results can be obtained from the fully connected layer. The effectiveness and generalization of the proposed model were validated on two real-world public datasets, ISO-NE and GEFCom2012. Experimental results indicate that the proposed model significantly improves the prediction accuracy in terms of evaluation metrics, compared with other contrast models.

This paper presents an intelligent inspection system for power insulators, integrating Unmanned Aerial Vehicles (UAVs) to address complex conditions. A novel image enhancement technique, based on the multi-scale Retinex algorithm with combined illumination correction and compensation, is proposed to enhance both the overall image quality of insulators and the visual perception for human observers. The study employs the state-of-the-art YOLOv7 target detector for automated identification of insulator defects. The YOLOv7 model undergoes further optimization, enhancing the feature extraction structure, incorporating the SE attention mechanism, and refining the loss function. These modifications enable the model to adapt to more intricate intelligent inspection tasks related to transmission line insulators, resulting in improved efficiency and detection accuracy. Building on this research out-comes, our team introduces an intelligent power insulator detection software system tailored for UAV inspection scenarios

Attention should be paid to the circadian rhythm effect, photobiological safety, and color gamut coverage (CGC) of displays. In this study, we use genetic algorithm to search the displays satisfying the demands of health, wide color gamut, and promise to improve working efficiency in the working conditions, when the correlated color temperature is around 6500 K. We also studied the variations in optimal results when changing the peak wavelength and full width at half maximum (FWHM), finding that the impacts of peak wavelengths are greater than those of FWHMs on the circadian action factor (CAF), blue light hazard, and CGC, and a balance should be made between the CAF and the luminous efficacy of radiation.

To investigate the laser-based displays that can offer the light source with a high level of photobiological safety and are suitable for users of different ages, this research optimizes the light source of the three-primary laser-based displays from its peak wavelength and the fractions of radiant flux. The non-linear program based on genetic algorithm is used to set the color gamut coverage over 90% and the correlated color temperature at 6500K. Find suitable peak wavelength and fractions of radiant flux with the premise of less than the blue light hazard (BLH) caused by the D65 light source to the 1 year, and select the result with the maximum circadian action factor (CAF) as the optimization for each age. Meanwhile, this research explores the effect that a change in the peak wavelength and fractions of radiant flux near the optimal results on CAF and BLH in different age groups. It provides reference for the design of health consideration laser displays for different age users in the daily working environment.