Advances in sensor technology and the Industrial Internet of Things (IIoT) have enabled the collection of large-scale monitoring data, facilitating intelligent remaining useful life (RUL) prediction for industrial equipment. However, accurate RUL prediction in distributed environments faces two major challenges. First, the scarcity of data and limited computational resources at edge clients hinder the development of robust RUL models, while privacy constraints prohibit centralized data sharing. Second, distribution shifts across client machines severely limit the model’s ability to generalize to unknown operating conditions (OCs) and equipment. To address these challenges, this article proposes a cloud-edge collaboration (CEC) federated invariance and specificity domain generalization (DG) (CEC-FedISDG) method. CEC-FedISDG integrates both domain-invariant and domain-specific predictive knowledge within a unified cloud-edge federated learning (FL) framework. This design enables the model to exploit the broad generalizability of invariant features while retaining domain-specific predictive capabilities. Specifically, a progressive invariance refinement (PIR) module is developed to gradually strengthen domain-invariant features while preserving privacy through a two-stage learning process. Subsequently, a dynamic specificity selection (DSS) module is designed. It dynamically integrates the outputs of private-domain regressors that retain domain specificity utilizing a domain classifier, adapting weights to test samples, thereby improving RUL prediction accuracy. Experimental evaluations on two bearing datasets and a real-world industrial wind turbine gearbox (WTG) dataset demonstrate that the CEC-FedISDG achieves superior generalization performance while adhering to strict privacy preservation requirements.

Accurate multi-energy load forecasting is crucial for the optimal operation of Integrated Energy Systems (IES). This study innovatively proposes a hybrid prediction model with two core innovations: (1) We design a periodic Huber loss function that dynamically adjusts penalty weights, effectively balancing load periodicity characteristics with outlier robustness; (2) We propose a stacked prediction architecture that combines the dilated convolution properties of Temporal Convolutional Network (TCN) with the bidirectional temporal modeling capabilities of Bidirectional Gated Recurrent Unit (BiGRU). By connecting multiple fundamental modules in series through residual connections, the proposed model achieves progressive extraction and prediction of multi-scale temporal features in multi-energy load sequences through gradual residual output optimization. Experiments conducted on the actual operational data from Arizona State University’s campus energy system demonstrate that the proposed model exhibits better performance than the advanced methods (Informer and FEDformer). The experimental results indicate the proposed model is effective for real-time operational scheduling of integrated energy systems.