Risk assessment method for power communication network based on LTA-EN

As the nerve center of power systems, power communication networks require risk assessment methods with lightweight architecture and high prediction accuracy to support reliable operation and maintenance decision-making. To overcome the limitations of conventional models—including weak temporal feature extraction, static weight assignment, and poor generalization in small-sample scenarios—this paper proposes a risk assessment method for power communication networks based on the fusion of Lightweight Temporal Attention (LTA) and Elastic Net (EN). First, a risk indicator system is constructed, and redundant features are eliminated through variance-based screening to reduce data dimensionality. The LTA module discards complex multi-head structures and computes dynamic weights solely by combining indicator-risk correlations and normalized interaction terms, enabling adaptive focusing on core time-series indicators. The EN regression is adopted to construct the prediction model, in which bi-regularization balances fitting performance and generalization ability to further improve prediction accuracy. Tests on 12-month small-sample datasets show that the MAE of the LTA-EN model is reduced by 23.4% compared with the conventional fixed-weight linear regression scheme and by 12.1% compared with the complex attention-ridge regression approach. The small-sample generalization error is decreased by 10% on average, and the core indicator recognition efficiency is improved by 40%. The model achieves an optimal tradeoff among small-sample adaptability, lightweight deployment, and high-precision prediction, and can provide efficient quantitative support for monthly risk early warning of power communication networks.