Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such as the balance between style and content, the model generalization ability, and diversity. This article first introduces the origin and development process of style transfer and provides a brief overview of existing methods. Next, this article explores research work related to style transfer, introduces some metrics used to evaluate the effect of style transfer, and summarizes datasets. Subsequently, this article focuses on the application of the currently popular deep learning technology for style transfer and also mentions the application of style transfer in video. Finally, the article discusses possible future directions for this field.

Unit commitment (UC) is a critical component for the power system dispatching departments. Current methodologies for solving UC problems predominantly rely on mixed-integer linear programming and are supplemented by data-driven approaches. These methodologies have two primary limitations: first, as the scale of the power grid expands, the complexity of algorithms increases sharply. Second, they fail to fully exploit grid topology information and historical data trends. To address these limitations, this article proposes a constrained graph-temporal convolutional network, which addresses the UC problem by directly predicting power output with constraints. The algorithm takes historical load data from grid nodes as input, utilizes graph convolutional networks to capture the physical grid topology information, and employs temporal convolutional networks to extract temporal features. Ultimately, the output of the graph-temporal convolutional network is projected into the feasible domain through a linear constraint activation layer, achieving accurate power prediction. Experiments conducted on the IEEE 30-BUS and IEEE 118-BUS systems validate the feasibility and superiority of our method in terms of accuracy and computational efficiency.

High-precision interpretable forecasting of multi-energy loads is crucial for planning and optimizing Integrated Energy Systems (IES). Current methods have three key limitations: 1) reliance on closed-box models obscures feature interactions; 2) traditional modal decomposition methods ignore cross-modal correlations and fail to efficiently extract global cycle-trend characteristics; 3) limitations in sampling frequency prevent capturing fast dynamic characteristics of energy loads. To address these, we propose a dual resolution channel multi-period cross reconstruction parallel closed-form continuous-time network (DMP-PCFC) for multi-energy load forecasting. This method includes a multi-period cross-reconstruction network which equivalent to the signal sifter for decoupling original sequences and nonlinear mapping across periods, and models hidden states of energy loads using neuron state iterative differential equations. A parallel Closed-form continuous-time neural network (PCFC) based on chunked parallelization reduces inference time from $O(T)$ (change with input sequence length) to $O(P)$ (fixed cycle length). To overcome inherent sampling resolution limitations, a dual-resolution channel model with weight-sharing PCFC is used for simultaneous inference. Extensive experiments and continuous wavelet transform (CWT) on the IES dataset at Arizona State University’s Tempe campus show that DMP-PCFC offers higher prediction accuracy and greater interpretability than existing methods, providing a new paradigm for downstream tasks based on other research methods, making it valuable for IES engineering applications. The code is publicly available at https://github.com/nuist-xf/DMP-PCFC

The joint optimisation of unit commitment and economic dispatch (ED) is one of the key issues in smart grid scheduling and control. Integrating the discrete on/off statuses of units in the unit commitment problem with the continuous active power outputs in ED significantly increases the overall complexity of the combined optimisation problem. We propose an innovative distributed algorithm based on a graph attention network to address this challenge. The graph neural network is used to extract the inter‐unit relational features and predict the future power dispatch schedule of each unit, while the parallel distributed coordination algorithm (PDCA), acting as the power dispatch algorithm, schedules and controls the output power of the units, including their start‐up and shut‐down states. Experimental results show that our algorithm performs well on both the IEEE 30‐bus and IEEE 118‐bus test systems, achieving a 1559 times speed boost compared to advanced solvers, and reaching economic optimality while satisfying all critical constraints to obtain an industrial acceptable solution.

With the intensification of global climate change, the frequent occurrence of typhoon disaster events has become a great challenge to the sustainable development of cities around the world; thus, it is of great significance to carry out the assessment of typhoon-directed economic losses. Typhoon disaster loss assessment faces key challenges, including complex regional environments, scarce historical data, difficulties in multi-source heterogeneous data fusion, and challenges in quantifying assessment uncertainties. Meanwhile, existing studies often overlook the complex relationship between the spatial expansion of urban and rural construction (SEURC) and typhoon disaster losses, particularly their differential manifestations across different regions and disaster intensities. To address these issues, this study proposes CLPFT (Comprehensive Uncertainty Assessment Framework for Typhoon), an innovative assessment framework integrating prototype learning and uncertainty quantification through a UProtoMLP neural network. Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. This reveals that implicitly classifying typhoon disaster loss types through prototype learning can significantly improve assessment accuracy in data-scarce scenarios. (2) By designing a dual-path uncertainty quantification mechanism, we realized high-reliability risk assessment, with 95.45% of actual loss values falling within predicted confidence intervals (theoretical expectation: 95%). This demonstrates that the dual-path uncertainty quantification mechanism can provide statistically credible risk boundaries for disaster prevention decisions, significantly enhancing the practical utility of assessment results. (3) Further investigation through controlling dynamic assessment factors revealed significant regional heterogeneity in the relationship between SEURC and directed economic losses. Furthermore, the study found that when typhoon intensity reaches a critical value, the relationship shifts from negative to positive correlation. This indicates that typhoon disaster loss assessment should consider the interaction between urban resilience and typhoon intensity, providing important implications for disaster prevention and mitigation decisions. This paper provides a more comprehensive and accurate assessment method for evaluating typhoon disaster-directed economic losses and offers a scientific reference for determining the influencing factors of typhoon-directed economic loss assessments.

Accurate and efficient multienergy load forecasting is fundamental for the secure and economic operation of integrated energy systems (IESs). However, current methods face several critical challenges as follows. 1) Insufficient perception of low-energy transient features leads to significant deviations in marginal scenario predictions. 2) Existing architectures struggle to simultaneously capture long-term trends and short-term dynamics efficiently. 3) Many high-accuracy models suffer from high computational complexity, limiting their practical deployment. To overcome these limitations, we propose the frequency-enhanced seasonal-trend recurrent neural network (FESTRNN), which incorporates two newly designed modules: the energy-enhanced spectral module to amplify critical frequency signals, and the dual-stream collaborative temporal forecasting module to efficiently model multiscale temporal dependencies. Experimental results on the ASU dataset demonstrate that FESTRNN achieves state-of-the-art accuracy across various forecasting horizons with significantly lower computational costs. This study provides a reliable basis for real-time scheduling in IES, offering strong practical value.