Energy consumption predictions for residential buildings play an important role in the energy management and control system, as the supply and demand of energy experience dynamic and seasonal changes. In this paper, monthly electricity consumption ratings are precisely classified based on open data in an entire region, which includes over 16 000 residential buildings. First, data mining techniques are used to discover and summarize the electricity usage patterns hidden in the data. Second, the particle swarm optimization-K-means algorithm is applied to the clustering analysis, and the level of electricity usage is divided by the cluster centers. Finally, an efficient classification model using a support vector machine as the basic optimization framework is proposed, and its feasibility is verified. The results illustrate that the accuracy and F-measure of the new model reach 96.8% and 97.4%, respectively, which vastly exceed those of conventional methods. To the best of our knowledge, the research on predicting the electricity consumption ratings of residential buildings in an entire region has not been publicly released. The method proposed in this paper would assist the power sector in grasping the dynamic behavior of residential electricity for supply and demand management strategies and provide a decision-making reference for the rational allocation of the power supply, which will be valuable in improving the overall power grid quality. INDEX TERMS Residential buildings, energy consumption prediction, clustering analysis, support vector machine.

In complex real-world situations, problems such as illumination changes, facial occlusion, and variant poses make facial expression recognition (FER) a challenging task. To solve the robustness problem, this paper proposes an adaptive multilayer perceptual attention network (AMP-Net) that is inspired by the facial attributes and the facial perception mechanism of the human visual system. AMP-Net extracts global, local, and salient facial emotional features with different fine-grained features to learn the underlying diversity and key information of facial emotions. Different from existing methods, AMP-Net can adaptively guide the network to focus on multiple finer and distinguishable local patches with robustness to occlusion and variant poses, improving the effectiveness of learning potential facial diversity information. In addition, the proposed global perception module can learn different receptive field features in the global perception domain, and AMP-Net also supplements salient facial region features with high emotion correlation based on prior knowledge to capture key texture details and avoid important information loss. Many experiments show that AMP-Net achieves good generalizability and state-of-the-art results on several real-world datasets, including RAF-DB, AffectNet-7, AffectNet-8, SFEW 2.0, FER-2013, and FED-RO, with accuracies of 89.25%, 64.54%, 61.74%, 61.17%, 74.48%, and 71.75%, respectively. All codes and training logs are publicly available at https://github.com/liuhw01/AMP-Net.

Acoustic emission (AE) technology can predict the occurrence of partial discharge (PD) faults, which is used to improve the safe operation level of gas-insulated switchgear (GIS) equipment. However, the strong noise interference from the production site is still the main factor affecting the identification accuracy. In this study, a simplified model is designed to approximate the accumulation of free metal particles on the surface of the GIS internal insulation structure, and white noise of various intensities is added to the collected PD-induced AE signals to simulate the background interference. The results prove that the proposed denoising method can achieve a better denoising effect in various signal-to-noise ratio (SNR) conditions. In particular, in the case of low SNR, the recognition accuracy of the accumulation degree of metal particles has been improved by more than 15%.

In order to promote the development and construction of smart cities, the massive equipment requirements of sensing terminals increased the pressure on urban site resource allocation. The light pole is suitable for carrying various urban functional equipment to form a smart street lighting system, which can provide rich site resources for the large-scale construction of urban functional facilities such as 5G micro base stations. However, the selection and combination of equipment mounted in the smart street lighting system only focus on the functional superposition at the physical level, without considering the relevance of each subsystem in practical application scenarios. Therefore, this study proposed a 5G micro base station location model based on a smart street lighting system. The correlation and cooperativity between 5G micro base stations and mounted devices were fully considered, and a universal system-level location selection index was developed to realize rational utilization of urban space site resources and intelligent linkage between subsystems. The results showed that the model is significantly effective for functional areas with different road network characteristics and provides practical, robust, effective, and accurate help for similar location selection problems.

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