The electric power industry currently experiences transformative technological and business changes caused by increased penetration of variable generation resources. Addressing these changes requires analytics applications using advanced forecasting models. In the electric power industry, our Predictive Analytics builds upon application-specific baseline models and scales these models up to big historical data sets. The Prescriptive Analytics rely on the predictive models to address specific business cases.
Our risk analytics include modeling of distribution tails that represent of extreme events. Such events could represent extreme (peak) power loads, extreme day-ahead forecast errors, extreme spot prices of electricity, or extreme weather events. Long tail distribution models are used in the practice of financial risk management and actuarial applications. We scale to these approaches to modeling of the entire probablity distributions and tail trends from the big data in the IIoT.
Our fleet reliability analytics has been proven in aerospace applications. The algorithms, data, and software are largely agnostic to the application and can be used for electric power systems assets: generating equipment, transmission equipment, substation equipment, and distribution systems. The main prerequisite is the existing ability to collect the asset management data such as repairs and replacement information combined with the asset serial number.