Real-time Machine Learning at the edge of power systems

The Monash Smart Energy City project provides a lens into the Clayton campus of Monash University, where distributed energy resources (DERs) appear as if they were instruments in a research laboratory.  An outcome of the project is the Distributed and Intelligent Power Systems (DIPS) platform (see our recent announcement). DIPS brokers the large volume of data from sensors in a dynamic power environment, enabling predictive analysis and archiving for later use. 
This blog is about the pilot DIPS experiment, which employs machine learning (ML) to forecast and estimate grid conditions for improved control and operational actions. Since machine learning can detect complex and abstract features directly from data without human biases, we wanted to explore a range of ML approaches to find the most suitable for forecasting at the edge of critical power systems.