The complex electricity consumption situation on the customer side and large-scale wind and solar power generation have gradually shifted the traditional “source-follow-load” model in the power system towards the “source-load interaction” model. At present, the voltage regulation methods require excessive computing resources to accurately predict the fluctuating load under the new energy structure. However, with the development of artificial intelligence and cloud computing, more methods for processing big data have emerged. This paper proposes a new method for electricity consumption analysis that combines traditional mathematical statistics with machine learning to overcome the limitations of non-intrusive load detection methods and develop a distributed optimization of cloud–edge–device distribution networks based on electricity consumption. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation prediction, it is proposed to use the long short-term memory method to process time series data, and an improved algorithm is developed in combination with error feedback correction. The R² value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4, respectively. Power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the regulation strategy, stability can be achieved after 25 iterations, and the optimal regulation is obtained. Finally, the cloud–edge–device distributed coevolution model of the power grid is obtained to achieve the economy of power grid voltage control.

Foundation models have emerged as a dominant paradigm in machine learning, enabling broad generalization and efficient adaptation across diverse tasks and domains. While this paradigm has achieved remarkable success in language and vision data, its extension to structured data remains far less understood. Foundation models for structured data are an emerging yet highly impactful research area with a rapidly growing body of literature. In this survey, we provide a systematic analysis of foundation models for structured data, focusing on tabular, time series, and graph data, covering over 150 representative methods. We analyze the intrinsic properties and inductive biases of structured data, clarify the core concepts of foundation models, and conduct an in-depth analysis of the key challenges that hinder the development of foundation models for structured data. Building on these insights, we organize existing approaches into a coherent taxonomy based on tokenization, architectures, pre-training objectives, and adaptation strategies. Finally, we discusse merging research directions and open problems, aiming to provide guidance toward more principled and scalable foundation models for structured data.