Whole building energy modelling (BEM) — physics-based simulation of building energy use — is a multipurpose tool for building energy efficiency and grid integration, supporting traditional applications like design, code compliance, and even code development. The U.S. Department of Energy (DOE) has invested in BEM and its applications since the 1970s, most recently and notably with EnergyPlus, its flagship open-source BEM engine.
BEM in general, and EnergyPlus in particular, are well established in applications such as high-performance building design, code compliance, and even code development — the latter done by evaluating the costs and savings of proposed amendments on a suite of fixed archetypal models of different building types including an office building, a school, a hospital, and high-rise apartments. These traditional applications are similar to one another in that they are primarily concerned with minimizing total annual energy use.
Python EMS allows EnergyPlus to integrate and exchange data with a large number of outside tools and libraries. Notable examples include the ability to use machine learning libraries in the implementation of building control algorithms, the ability to acquire real-time data from equipment and building control systems, and the ability to be embedded in larger real-time applications that require fine-grain simulation control.