Diego Cerrai
Assistant Professor/Civil and Environ Engineering
Storrs Mansfield
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Scholarly Contributions
57 Scholarly Contributions
Application of Machine Learning Algorithms for Wind Gust Prediction: a Comparison between WRF and AI.
2023
Research Type: Poster/Presentation
Predicting Weather Related Power Outages in the Northeast United States
2023
Research Type: Poster/Presentation
Granular Estimation of Wintry Precipitation Type from Image Data.
2023
Research Type: Poster/Presentation
Changes of Electric Distribution Network Storm Outages in Future Climate Scenarios: Evaluation for a Service Territory in Northeastern United States
2023
Research Type: Poster/Presentation
Advancing Snowfall Prediction in the Northeast United States: An Integrated Machine Learning and Numerical Weather Modeling Approach
2023
Research Type: Poster/Presentation
Assessing Power Distribution Resilience Hardening Effectiveness Using Combined Physics-Based and Data-Driven Modeling
2023
Research Type: Poster/Presentation
Storm Power Outage Prediction and Verification using NWP Models and Remote Sensing Data
2024
Research Type: Conference Proceedings
A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package
2024
Research Type: Journal Article
Assessing Grid Hardening Strategies to Improve Power System Performance During Storms Using a Hybrid Mechanistic-Machine Learning Outage Prediction Model
2024
Research Type: Journal Article
Integrating Structural Vulnerability Analysis and Data-Driven Machine Learning to Evaluate Storm Impacts on the Power Grid
2024
Research Type: Journal Article
A Probabilistic Method for Integrating Physics-Based and Data-Driven Storm Outage Prediction Models for Power Systems
2024
Research Type: Journal Article
Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts
2024
Research Type: Journal Article
Autoregressive Modeling of Utility Customer Outages with Deep Neural Networks
Research Type: Conference Proceedings
Machine Learning Methods to Approximate Rainfall and Wind from Acoustic Underwater Measurements
Research Type: Journal Article