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With the rapid expansion of urban power grids and the widespread adoption of renewable energy sources, conventional fault-diagnosis methods for power grids face significant challenges in handling the complexities of modern, highly interconnected systems. While artificial intelligence approaches offer promising alternatives for power grid fault diagnosis, the scarcity of labeled grid data severely hinders the application of AI techniques in this domain. To tackle these issues, this study introduces a semi-supervised learning framework featuring self-supervised learning and an adaptive threshold mechanism (SAT-SSL) designed for fault detection and classification in power grids. Compared with existing approaches, the proposed method substantially reduces reliance on labeled data while maintaining excellent recognition performance. First, a frequency-domain analysis is applied to power grid data to identify and filter anomalous events, which are then classified and labeled based on their visual characteristics, thereby constructing a dedicated power grid dataset. Next, the Yule–Walker algorithm is employed to extract features from the power grid data. A semi-supervised learning framework is subsequently developed, integrating a self-supervised loss and a dynamic threshold mechanism to enhance the model’s information extraction capacity and adaptability across diverse operational scenarios. Finally, the constructed power grid dataset, together with two standard benchmark datasets, is utilized to evaluate the model’s effectiveness. Experimental results demonstrate that the proposed model maintains a low error rate across multiple scenarios and varying label proportions. Specifically, on the power grid dataset, retaining only 5% of the labels yields an error rate of just 6.15%, confirming the method’s ability to achieve precise fault detection and classification even with very limited labeled data.

Platforms based on digital twins are gaining widespread use in overseeing energy infrastructure, particularly within smart grid environments. Recent progress in artificial intelligence opens up new possibilities to build greater user confidence by strengthening predictive and prescriptive analysis capabilities and refining how users interact with these systems. This study introduces an innovative framework and architectural design for energy digital twins that combines data-driven techniques with knowledge-based elements. Machine learning-driven data integration and analysis are fused into a knowledge graph that captures asset condition information, forecasting results, and relevant domain expertise. This structure underpins a retrieval-augmented generation strategy, which powers an advanced conversational virtual assistant grounded in a large language model. The assistant delivers practical decision-making support for asset oversight and maintenance activities. Key parts of the suggested architecture have been aligned with readily available commercial tools to create a working prototype. This prototype was applied in a practical scenario involving a portion of the high-voltage energy network located in central Italy.

Accurate short-term forecasting of household electricity consumption is vital to contemporary distributed energy networks. Most approaches to load prediction rely on deep neural architectures and require access to confidential, user-specific consumption records during training. Traditional neural network training typically collects all available data at a single central location to develop one unified model. Nevertheless, pooling individual user information in this manner raises serious concerns regarding data security and personal privacy. To address these issues, the present research explores federated learning techniques combined with model personalization as promising alternatives. Through a comprehensive simulation-based evaluation, these approaches are benchmarked against the standard centralized training strategy and a simple pre-trained reference model. The analysis reveals that the intrinsic characteristics and heterogeneity of the underlying electricity consumption patterns significantly affect the resulting model error levels. Consequently, directly contrasting error distributions across methods largely reflects differences in data composition rather than true differences in algorithmic effectiveness. To overcome this limitation, the study introduces a novel evaluation technique termed “differential comparison.” This approach focuses exclusively on pairwise loss differences across identical data instances, thereby enabling a fairer assessment of relative model improvements via the resulting difference distributions. Applying this method demonstrates that personalized model training outperforms all other strategies examined for residential load prediction, achieving better results in 59.1% of the evaluated scenarios.