Commercial building owners spend 30% of their operating budget on energy. Costs can be reduced with improved building energy management practices. Optimizing building performance also reduces demand for energy from the grid, lowers carbon emissions from electricity generation and fuels burned on site, and can improve occupant comfort.

Today, buildings can fall anywhere along the continuum of data management and analytics: trending, benchmarking, fault detection and diagnostics, forecasting, customized software applications, cloud computing data analysis.

Fault Detection and Diagnostics (FDD) analysis enables ongoing monitoring-based commissioning of building systems to save energy and extend equipment life. Faults relate to a system’s performance, meaning the system is operating but is performing sub-optimally. FDD tools acquire electricity, gas, steam, temperature, humidity, or chilled water energy consumption data, typically at the building level via an energy management platform, and compare those values to expected baseline energy consumption levels. The tool flags building energy consumption as high when it exceeds the baseline value by a predetermined threshold (to avoid false alarms). Often, the baseline takes into account one or more key explanatory variables, such as outdoor air temperature, building and equipment schedules, time of day or year, temperature set points, and plant configurations. Table 1 describes the broad range of possible baselines for FDD in order of increasing complexity. FDD is an important type of analysis to complete because inefficient equipment operation attributed to inadequate initial commissioning, operational issues, and real time performance degradation can waste an estimated 15 to 30% of energy used in commercial buildings. However, current FDD methods are limited to classification and pattern recognition to detect and diagnose faults.