Residential demand response is vital for the efficiency of power system. It has attracted much attention from both academic and industry in recent years. Accurate short-term load forecasting is a fundamental task for demand response. While short-term forecasting for aggregated load data has been extensively studied, load forecasting for individual residential users is still challenging due to the dynamic and stochastic characteristic of single users’ electricity consumption behaviors, i.e., the variability of the residential activities. To address this challenge, this paper presents a short-term residential load forecasting framework, which makes use of the spatio-temporal correlation existing in appliances’ load data through deep learning. Multiple time series are conducted in the framework to describe electricity consumption behaviors and their internal spatio-temporal relationship. And a method based on deep neural network and iterative ResBlock is proposed to learn the correlation among different electricity consumption behaviors for short-term load forecasting. Experiments based on real world measurements have been conducted to evaluate the performance of the proposed forecasting approach. The results show that both the appliances’ load data and iterative ResBlocks can help to improve the forecasting performance. Compared with existing methods, measurements on Root Mean Squared Error, Mean Absolute Error and Mean Absolute Percentage Error for the proposed approach are reduced by 3.89%-20.00%, 2.18%-22.58% and 0.69%-32.78%. In addition, further experiments are conducted to evaluate the impact of using appliances' load data, iterative ResBlocks as well as other factors for the proposed approach.

Weakly supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitting different distributions. However, due to the limited number of annotated anomaly samples, directly training networks with the discriminative loss may not be sufficient. To overcome this issue, this article proposes a novel strategy to transform the input data into a more meaningful representation that could be used for anomaly detection. Specifically, we leverage an autoencoder to encode the input data and utilize three factors, hidden representation, reconstruction residual vector, and reconstruction error, as the new representation for the input data. This representation amounts to encode a test sample with its projection on the training data manifold, its direction to its projection, and its distance to its projection. In addition to this encoding, we also propose a novel network architecture to seamlessly incorporate those three factors. From our extensive experiments, the benefits of the proposed strategy are clearly demonstrated by its superior performance over the competitive methods. Code is available at: https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection .

Internet of Things (IoT) time-series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security. Nevertheless, the complex spatial–temporal dynamics and high dimensionality of IoT time series make the analysis increasingly challenging. In recent years, the powerful feature extraction and representation learning capabilities of deep learning (DL) have provided an effective means for IoT time-series analysis. However, few existing surveys on time series have systematically discussed unsupervised DL-based methods. To fill this void, we investigate unsupervised DL for IoT time series, i.e., unsupervised anomaly detection and clustering, under a unified framework. We also discuss the application scenarios, public data sets, existing challenges, and future research directions in this area.

Multispectral and Hyperspectral Image Fusion (MHIF) involves the fusion of high spatial resolution multispectral images (HR-MSI) and low spatial resolution hyperspectral images (LR-HSI) to generate high spatial resolution hyperspectral images (HR-HSI), has gained significant attention in the field of remote sensing imaging. While CNN and Transformer models have shown effectiveness in MHIF, existing CNN or Transformer-based algorithms are overburdened with model size, making it difficult to achieve an effective trade-off between fusion accuracy and degree of lightweight. Recently, Implicit Neural Representation (INR) has been proven good interpretability and the ability to exploit coordinate information in 2D tasks. Nonetheless, INR-based fusion networks have certain limitations, such as the need for deeper super-resolution networks as shallow encoders, and insufficient representation capability on high upsampling ratios. To address these challenges, we present the Quadtree Implicit Sampling (QIS), which employs a hierarchical sampling from the perspective of the quadtree, to enhance the capacity of the overall network. Furthermore, the remarkable design of QIS allows us to adopt a lightweight structure as the shallow encoder, greatly alleviating the network burden and achieving lightweight. Inspired by generative adversarial models, we incorporate QIS as a lightweight generator into the GAN framework named QIS-GAN and leverage a discriminator to increase the fidelity of fused images. The results showcase the superior performance of QIS-GAN on the MHIF tasks with upsampling ratios of ×4, ×8, and ×16, surpassing the state-of-the-art in several datasets. The code for our approach will be available at https://github.com/chunyuzhu/QIS-GAN.

Unmanned aerial vehicles (UAVs) are emerging as promising devices to provide valuable information in rescue applications, which can be dispatched to take photos for Points Of Interests (POIs) in disaster areas where humans are hard to approach. Most existing studies focused on the limited energy capacity issue of UAVs when they take photos, which however ignored an important fact, that is, the photos taken by the UAVs usually are highly redundant. In this paper we study a novel monitoring quality maximization problem to find a flying tour for an energy-constrained UAV, such that the amount of non-redundant information of the photos taken by the UAV in its tour is maximized. Due to NP-hardness of the problem, we first propose an approximation algorithm with a quasi-polynomial time complexity. We then devise a fast yet scalable heuristic algorithm for the problem. We finally evaluate the performance of the proposed algorithms via both a real dataset and extensive simulations. Experimental results show that the proposed algorithms are very promising. Especially, the amounts of non-redundant information by the proposed approximation and heuristic algorithms are about 11% and 8% larger than that by the state-of-the-art, respectively. To the best of our knowledge, we are the first to consider the novel problem of collecting non-redundant information with an energy-constrained UAV.

The thermal energy storage properties of a working fluid can be modified by the exothermic and endothermic adsorption and desorption of fluid molecules in the micro/nanoporous materials. In this study, thermogravimetric (TG) analysis experiments and molecular simulations (molecular dynamics, MD, and grand canonical Monte Carlo, GCMC) were employed to examine the thermal energy storage properties of the UIO-66 metal organic framework material, UIO-66/H₂O nanofluids and pure water. Our results showed that the molecular simulation calculations were, in principle, consistent with the obtained experimental data. The thermal energy storage performance of UIO-66/H₂O nanofluids was enhanced with the increase in the UIO-66 mass fraction. In addition, the differences between the simulation calculations and experimental results could be mainly ascribed to the different structures of UIO-66 and the evaporation of fluid samples. Furthermore, this work indicated that molecular simulations contributed to developing novel working pairs of metal organic heat carriers (MOHCs).