Accurate forecasting of household energy load and generation is critical for energy management systems, especially in the context of the rapid development of smart grids and renewable energy. However, privacy concerns often arise when handling sensitive household energy data. Federated learning (FL), as a privacy-preserving distributed learning method, enables collaborative model training across households without exposing sensitive energy data. Nevertheless, traditional federated learning still faces challenges in meeting the personalized needs of households due to the differences in consumption patterns among households, which affects the forecasting accuracy. In this paper, we propose a novel personalized FL method that combines mixture of expert and conformal predictions to improve forecasting accuracy while quantifying prediction uncertainty. Our method utilizes personalized federated learning (PFL) to develop a personalized model for each household that captures its unique consumption behavior. The mixture of experts dynamically integrates global and local personalized models to enhance prediction and adaptability. In addition, uncertainty quantification is achieved through conformal prediction, providing reliable prediction interval. Experiments conducted on two real-world household energy datasets demonstrate that our method outperforms existing approaches in terms of prediction accuracy and uncertainty assessment.

Deep learning has become increasingly important in fault diagnosis, but it relies on a large amount of high-quality labeled data. Collecting data from distributed machines can expand the dataset, but it usually leads to privacy concerns. Moreover, since the operating conditions are complex in real-world applications, the collected training data and the test data often have different distributions. Therefore, a well-trained model on the training data may not be suitable for test data due to the domain shift. To preserve privacy and to mitigate the domain shift, in existing federated transfer learning fault diagnosis methods, distributed machines exchange model parameters and features rather than raw data with the central server. However, such methods suffer from a single point of failure and high communication burden. To address these issues, we propose a fully decentralized federated transfer learning fault diagnosis method. More specifically, the proposed method obtains a pretrained model among source nodes with labeled training data where each source node exchanges model parameters with its neighboring source nodes. Moreover, a novel transfer learning strategy is proposed, which aligns features of test data at the target node with features of training data at its connected source nodes to mitigate misclassifications resulting from the domain shift. The effectiveness of the proposed method is verified by various experiments on two public bearing datasets.

Tropical cyclones (TC) bring enormous harm to human beings, and it is crucial to accurately forecast the intensity of TCs, but the progress of intensity forecasting has been slow in recent years, and tropical cyclones are an extreme weather phenomenon with short duration, and the sample size of TC intensity series is small and short in length. In this paper, we devolop a tensor ARIMA model based on feature reconstruction to solve the problem, which represents multiple time series as low-rank Block Hankel Tensor(BHT), and combine the tensor decomposition technique with ARIMA for time series prediction. The method predicts the sustained maximum wind speed and central minimum pressure of TC 6-24 hours in advance, and the results show that the method exceeds the global numerical model GSM operated by the Japan Meteorological Agency (JMA) in the short term. We further checked the prediction results for a TC, and the results show the validity of the method.

Tropical cyclones (TCs) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave (PMW) rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to low temporal resolution of microwave sensors. This study attempts to solve this problem by directly predicting PMW rainfall images (PMRIs) from satellite infrared (IR) images of TC. We develop a generative adversarial network (GAN) to simulate PMRI using IR images and establish the mapping relationship between TC cloud-top brightness temperature and PMR, and the algorithm is named tropical cyclone rainfall (TCR)-GAN. Meanwhile, a new dataset that is available as a benchmark, Dataset of TC IR-to-Rainfall Prediction (TCIRRP), was established, which is expected to advance the development of artificial intelligence in this direction. The experimental results show that the algorithm can effectively extract key features from IR. The end-to-end deep learning approach shows potential as a technique that can be applied globally and provides a new perspective TC precipitation prediction via satellite, which is expected to provide important insights for real-time visualization of TC rainfall globally in operations.

Tropical cyclone (TC) is one of the major meteorological disasters in the world, which seriously threatens the safety of human life and property. In this study, we adopt the tropical cyclone best track archive and the global tropical cyclone exposure data sets, using the ensemble machine learning approach for exposed to the TCs of the population and asset to estimate. To the best of our knowledge, this research is the first hazards assessment study using machine learning methods to estimate the exposure of tropical cyclones to population and property end-to-end. In particular, we validated our model using data from the country most affected by tropical cyclones. The results depicted a 10% improvement over traditional methods. The correlation coefficient r between the predicted value and the exposure data is 0.813, demonstrating a strong correlation between the predicted results and the great potential of machine learning to solve the problem of hazards assessment. However, there is still uncertainty in assessing the impact of tropical cyclones, especially the impact caused by high wind speeds.

Cyclone, especially tropical cyclones, are one of the most significant meteorological disasters in the world, which seriously threaten the safety of life and property. The ability to accurately identify the type and intensity of cyclones is crucial for disaster prevention. This article proposes the use of dual branches Convolutional Neural Network (CNN) model, based on Global Forecast System Analysis (GFS) to identify cyclones, including tropical cyclones, extratropical cyclones and subtropical cyclones, a total of 11 types of cyclone-related phenomena. The model can learn spatial information and extract crucial features, and merge at the end to achieve end-to-end prediction output. The results indicate that the model's identify accuracy of tropical cyclones and extratropical cyclones exceeds 90%, and the identify accuracy of cyclones disturbances and subtropical cyclones is also more than 75%, the model does not require expert knowledge, and the speed of operation fast.

The accurate estimation of tropical cyclone (TC) size is one of the key steps in TC forecasting and disaster warninglmanagement. In this study, we proposed the use of deep convolutional neural networks (CNN) to estimate the size of tropical cyclone. To the best of our knowledge, this is the first study to estimate the size of tropical cyclones using deep learning methods; we use about 1,000 tropical cyclones which contain about 30,000 infrared remote sensing images as the data set. Compared with the best track archives, the mean error of our proposed model is 24nmi, the error is even smaller than the Multiplatform Tropical Cyclone Surface Winds Analysis (MTCSWA) operated by National Oceanic and Atmospheric Administration(NOAA), which shows the great potential of deep learning in estimating the size of tropical cyclones.

The prediction of tropical cyclones (TCs) is a valuable and challenging task. As a research hotspot of deep learning, generative adversarial network (GAN) has obtained promising results in TC prediction recently. However, different kinds of GAN applied in meteorology usually focus on how to generate high-quality images, but ignore how GAN learns the physical characteristics in the training process. This paper visualizes the intermediate results of GAN in the training process, and shows the process of learning the physical characteristics of data distribution similar to TC images for GAN. The core method in this paper is deep convolutional generative adversarial networks (DCGAN). In order to obtain prediction results with interpretable physical characteristics, we propose two training strategies for DCGAN, namely the long short-term training method and training parameters selection according to physical characteristics. Experimental results show that the DCGAN model and two strategies proposed in this paper have good performance in the visual prediction of TCs.