Along with the proliferation of electric vehicles (EVs), optimizing the use of EV charging space can significantly alleviate the growing load on intelligent transportation systems. As the foundation to achieve such an optimization, a spatiotemporal method for EV charging demand prediction in urban areas is required. Although several solutions have been proposed by using data-driven deep learning methods, it can be that these performance-oriented approaches may struggle to correctly understand the underlying factors influencing charging demand, particularly charging prices. A representative case that highlights the challenge faced by existing methods is their potential misinterpretation of high prices during peak times, leading to an incorrect assumption that higher prices correspond to increased demand. To address the challenges associated with training an accurate and reliable prediction model for EV charging demand, this paper proposes a novel approach called PAG, which leverages the integration of graph and temporal attention mechanisms for effective feature extraction and introduces physics-informed meta-learning in the pre-training step to facilitate prior knowledge learning. Evaluation results on a dataset of 18,061 EV charging piles in Shenzhen, China, show that the proposed approach can achieve state-of-the-art forecasting performance and the ability to understand the adaptive changes in charging demands caused by price fluctuations.

The recent surge in electric vehicles (EVs), driven by a collective push to enhance global environmental sustainability, has underscored the significance of exploring EV charging prediction. To catalyze further research in this domain, we introduce UrbanEV — an open dataset showcasing EV charging space availability and electricity consumption in a pioneering city for vehicle electrification, namely Shenzhen, China. UrbanEV offers a rich repository of charging data (i.e., charging occupancy, duration, volume, and price) captured at hourly intervals across an extensive six-month span for over 20,000 individual charging stations. Beyond these core attributes, the dataset also encompasses diverse influencing factors like weather conditions and spatial proximity. Comprehensive experiments have been conducted to showcase the predictive capabilities of various models, including statistical, deep learning, and transformer-based approaches, using the UrbanEV dataset. This dataset is poised to propel advancements in EV charging prediction and management, positioning itself as a benchmark resource within this burgeoning field.

Heterogeneous feature spaces and technical noise hinder the cellular data integration and imputation. The high cost of obtaining matched data across modalities further restricts analysis. Thus, there’s a critical need for deep learning approaches to effectively integrate and impute unpaired multi-modality single-cell data, enabling deeper insights into cellular behaviors. To address these issues, we introduce the Modal-Nexus Auto-Encoder (Monae). Leveraging regulatory relationships between modalities and employing contrastive learning within modality-specific auto-encoders, Monae enhances cell representations in the unified space. The integration capability of Monae furnishes it with modality-complementary cellular representations, enabling the generation of precise intra-modal and cross-modal imputation counts for extensive and complex downstream tasks. In addition, we develop Monae-E (Monae-Extension), a variant of Monae that can converge rapidly and support biological discoveries. Evaluations on various datasets have validated Monae and Monae-E’s accuracy and robustness in multi-modality cellular data integration and imputation.

Recent booming successes of electric vehicles (EVs) motivate emerging exploration of spatio-temporal EV charging demand forecasting to inform policy making. Recent studies have contributed to remarkable accuracy improvement by developing deep learning methods. However, when they access massive amounts of data and frequently exchange data through the Internet of Things (IoT), data silos and inefficient training emerge as main challenges. To tackle these challenges, this study proposes an integrated approach for regional EV charging demand forecasting, named FMGCN, which consists of two modules, namely 1) Spatio-temporal Learning module, which introduces spatial and temporal attentions to capture the underlying charging patterns between different regions and cities effectively; and 2) Distributed Pretraining module, which incorporates Federated Learning and Meta-Learning to enhance the adaptivity and generalisability of the forecasting model. A comprehensive evaluation based on a real-world dataset of 25,246 public EV charging piles shows that the proposed model outperforms other representative models with 1) an average improvement of 29.9% in forecasting errors; 2) an acceleration of 65% in convergence speed; and 3) a sound adaptability to support varying charging demand.

Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as smart surveillance and risk management with unprecedented capabilities. Nevertheless, MTAD is facing critical challenges deriving from the dependencies among sensors and variables, which often change over time. To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships. We combine adaptive graph learning methods with graph attention to generate a global-local graph that can represent both global correlations and dynamic local correlations among sensors. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module to construct a coupled attention module. In addition, we develop a multilevel encoder-decoder architecture that accommodates reconstruction and prediction tasks to better characterize multivariate time series data. Extensive experiments on real-world datasets have been conducted to evaluate the performance of the proposed CAN approach, and the results show that CAN significantly outperforms state-of-the-art baselines.