Energy analytics generally describes the process of collecting electrical data and applying sophisticated analytical software and algorithms to deliver insights around consumption and time of use reductions.
The most simple source of energy data is a building's utility meter, also known as the master meter. Many times, this data stream only provides one data point per month in the form of the utility bill. There are very few insights that can be derived from such a high-level analysis.
Most building automation systems (BAS) pick up meters as data points and can export consumption data in standard formats, e.g., via BACnet to an analytics application. For buildings without a BAS, a router or data logger can access data and communicate to systems over the Internet or building networks.
Today, more sophisicated IoT devices and submeters are being deployed to collect electrical data in real time. This data can be fed into a building monitoring system to uncover much more specific and actionable insights from the energy analytics. This increased granularty, both with shorter intervals and more end points, has made energy analytics an indispensible tool in building operations management.
As algorithms are fed more aggregate data, energy analytics are being combined with machine learning to ensure maximimum performance, optimal scheduling, equipment fault detection, and peak demand load shifting and shaving.