Deploying ML & Optimization In Risk Assessment Analytics for Robustness and Resilience
We explore how ML & optimization can aid the risk assessment and management process with an eye towards robustness and resiliency.
Mosaic Data Science’s machine learning for utilities optimizes energy distribution, predicts maintenance needs, and enhances efficiency like never before. We help customers say goodbye to wastage and hello to precision as algorithms adapt to consumption patterns, ensuring seamless power supply and resource allocation.
We explore how ML & optimization can aid the risk assessment and management process with an eye towards robustness and resiliency.
This white paper explores how we can integrate third-party weather data and utility-specific asset prioritization requirements to build more relevant optimization tools for scheduling the inspection and potential repair of grid anomalies.
Operating in today’s conditions requires creative thinking, agile decision making and embracing change. AI & Digital Transformation can support all these disciplines.
Machine learning provides an excellent avenue for predicting future energy consumption. Accurate insights can provide critical insights into variables affecting the demand, providing decision-makers with an opportunity to address these levers. Forecasts also provide a benchmark to identify anomalous behavior, either high/low consumption, and alert managers to faults within the building.
Mosaic developed a data-driven alerting solution powered by unsupervised learning to assist a leading energy utility in detecting voltage anomalies & informing optimal grid health decisions.
Mosaic developed an innovative optimization app for the green power sales function at a leading utility, helping them recommend suites of renewable energy products to meet corporate carbon footprint reduction goals within budgetary constraints.
Mosaic designed and deployed custom computer vision models to automate asset recognition & inform inspection decisions.