Commercial building owners spend 30% of their operating budget on energy. Costs can be reduced with improved building energy management practices. Optimising 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.

Advanced information technology has expanded the volume, velocity and variety of data, or “big data” that can be collected. Today, more detailed and more regular intervals of data can be collected to complete analyses on the performance of specific building systems. With more accurate, complete, and consistent data and analysis, energy management decisions related to specific building systems can be made proactively to run systems efficiently, lowering operating costs, extending equipment life, and improving occupant comfort. With this level of data collected from the utility, a building automation or BEMS, and smart meters, several analyses can be completed to learn what equipment is running sub-optimally and to identify the types of energy efficiency projects in which to invest, at what level of investment. Analytical results should be shared consistently with building management staff to inform decision-making on building systems, like lighting, heating, and cooling, and to support choices of energy efficiency projects.

Fuzzy logic ensures to endow current BEMSs with built-in intelligence capable of improving their performance in managing complex infrastructures consisting of HVACs, refrigerators, PV plants, pumps, etc. These tools intend to replace human data analysis and interpretation with software that attempts to mimic human analytical procedures through expert rules by detecting and diagnosing problems without user intervention as well as predicting future maintenance needs through short and medium-term energy consumption data, logging conditions, reports, prior FDD analysis, etc.