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Discovering urban functional zones (UFZs) is critical for understanding city spatial structures and supporting effective urban planning. Existing approaches to UFZ discovery typically rely on one of three costly strategies: (1) training large vision models directly on satellite imagery, which demands substantial computational resources; (2) leveraging crowdsourced data such as Points of Interest (POIs) from platforms like OpenStreetMap, which may be incomplete, inconsistent, or unavailable in many regions; or (3) collecting custom labeled data, which requires significant time, expense, and expert effort. Recently, large multi-modal models (LMMs) emerged as a promising alternative, offering strong capabilities in interpreting visual content without requiring extensive data labeling. However, their performance remains limited when applied to the UFZ discovery task, often struggling to capture the complex spatial and functional details and interactions of urban regions. To address this challenge, we propose a new approach that enhances LMMs’ reasoning capability to recognize urban functional zones by keeping LMM encoders frozen while training only lightweight graph-based models, eliminating the need for LMM fine-tuning or additional pre-training. Specifically, our approach first partitions the target area into small regions by road network, where an LMM is used for each region to generate visual and textual embeddings independently using its image and text encoders. Then, two graphs are constructed in which nodes represent regions with features defined by their respective embeddings, and edges encode their spatial adjacency. Message-passing on the two graphs hence captures spatial correlation between the visual and textual modalities. After that, graph clustering will suggest the prototypes representing nearby zones with similar urban functions, where contrastive learning is further leveraged to encourage cross-modal consistency. Evaluation on four city districts, namely Philadelphia (PA, USA), Pudong (Shanghai, China), San Francisco (CA, USA), and Seattle (WA, USA), substantiates the effectiveness of our proposal and that its performance is on par with supervised competitors. 

ABSTRACT With renewable energy being aggressively integrated into the grid, energy supplies are becoming vulnerable to weather and the environment, and are often incapable of meeting population demands at a large scale if not accurately predicted for energy planning. Understanding consumers' power demands ahead of time and the influences of weather on consumption and generation can help producers generate effective power management plans to support the target demand. In addition to the high correlation with the environment, consumers' behaviors also cause non‐stationary characteristics of energy data, which is the main challenge for energy prediction. In this survey, we perform a review of the literature on prediction methods in the energy field. So far, most of the available research encompasses one type of generation or consumption. There is no research approaching prediction in the energy sector as a whole and its correlated features. We propose to address the energy prediction challenges from both consumption and generation sides, encompassing techniques from statistical to machine learning techniques. We also summarize the work related to energy prediction, electricity measurements, challenges related to energy consumption and generation, energy forecasting methods, and real‐world energy forecasting resources, such as datasets and software solutions for energy prediction. This article is categorized under:Application Areas > Industry Specific ApplicationsTechnologies > PredictionTechnologies > Machine Learning 

Graph Neural Networks (GNNs) have shown superb performance in handling networked data, mainly attributed to their message passing and convolution process across neighbors. For most literature, the performance of GNNs is mainly reported based on noise-free data environments. No study has systematically evaluated GNNs’ performance under noise. In this article, we carry out an empirical study and theoretical analysis of four types of GNNs, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Contrastive Networks (GCL), and graph UniFilter under three types of noise, including attribute noise, structure noise, and label noise. Our study shows that GNNs behave tremendously differently in response to different types of noise. Overall, GAT is the most noise vulnerable and sensitive, whereas GCL is the most noise resilient. We further carry out theoretical analysis to explain the reason causing GAT to be sensitive to noise, and propose a solution to enhance its noise resilience. Our study brings in-depth firsthand knowledge of GNNs under noise for researchers and practitioners to better utilize GNNs in real-world applications.