Multi-Scale Temporal Analysis for Failure Prediction in Energy Systems

Many existing models struggle to predict nonlinear behavior during extreme weather conditions. This study proposes a multi-scale temporal analysis for failure prediction in energy systems using PMU data. The model integrates multi-scale analysis with machine learning to capture both short-term and long-term behavior. PMU data lacks labeled states despite logged failure records, making it difficult to distinguish between normal and disturbance conditions. We address this through: (1) Extracting domain features from PMU time series data; (2) Applying multi-scale windows (30s, 60s, 180s) for pattern detection; (3) Using Recursive Feature Elimination to identify key features; (4) Training multiple machine learning models. Key contributions: Identifying significant features across multi-scale windows; Demonstrating LightGBM's superior performance (0.896 precision); Showing multi-scale analysis outperforms single-window models (0.841). Our work focuses on weather-related failures, with plans to extend to equipment failure and lightning events.

A Probabilistic Domain-knowledge Framework for Nosocomial Infection Risk Estimation of Communicable Viral Diseases in Healthcare Personnel: A Case Study for COVID-19

Hospital-acquired infections of communicable viral diseases (CVDs) are posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of hospital-acquired infections, and subsequently higher rates of morbidity and mortality. We proposed a domain knowledge-driven infection risk model to quantify the individual HCP and the population-level healthcare facility risks. For individual-level risk estimation, a time-variant infection risk model is proposed to capture the transmission dynamics of CVDs. At the population-level, the infection risk is estimated using a Bayesian network model constructed from three feature sets including individual-level factors, engineering control factors, and administrative control factors. The sensitivity analyses indicated that the uncertainty in the individual infection risk can be attributed to two variables: the number of close contacts and the viral transmission probability. The model validation was implemented in the transmission probability model, individual level risk model, and population-level risk model using a Coronavirus disease case study. Regarding the first, multivariate logistic regression was applied for a cross-sectional data in the UK with an AIC value of 7317.70 and a 10-fold cross validation accuracy of 78.23%. For the second model, we collected laboratory-confirmed COVID-19 cases of HCP in different occupations. The occupation-specific risk evaluation suggested the highest-risk occupations were registered nurses, medical assistants, and respiratory therapists, with estimated risks of 0.0189, 0.0188, and 0.0176, respectively. To validate the population-level risk model, the infection risk in Texas and California was estimated. The proposed model will significantly influence the PPE allocation and safety plans for HCP