Large Scale predictive analytics for real-time energy management

As demand for cost-effective energy and resource management continues to grow, intelligent automated building solutions are necessary to reduce energy consumption, increase alternative energy sources, reduce operational costs and find interoperable solutions that integrate with legacy equipment without massive investments in new equipment and tools. The ability to analyze, understand and predict building behavior offer tremendous opportunities to demonstrate and validate increased energy efficiencies, which may ease many particular exorbitant pressures taxing the grid. In this paper, we describe a research platform driven by an existing campus microgrid for developing large scale, predictive analytics for real-time energy management.