Why this use case matters
Repair scheduling optimization is possible by integrating third-party weather data, and grid asset prioritization into the decision process. Machine learning enables more relevant routing objectives when developing inspection and repair schedules.
Optimization, Supervised Learning – regression, classification
Tabu search, Tree-Based Models (XGBOOST, LightGBM, Random Forest), Neural Nets, LSTM, Stochastic Forecasting, Bracketing
An ML-based solution can help dispatchers make better decisions about scheduling inspections and repairs to more effectively ensure grid health and prevent outages for customers. Save valuable time, money, and resources that drive operational efficiency and dollar savings.