Approximating model predictive control with existing building simulation tools and offline optimization

摘要

Model predictive control (MPC) is an established control technique in other fields and holds promise for improved controls in high-performance buildings. It has been receiving increasing attention in buildings research but has yet to find its way into common practice. This is due, at least in part, to a mismatch between the tools and techniques used in most MPC development and those commonly found in building design and operation. This article investigates the use of offline optimization with common building simulation tools to approximate MPC with lookup tables. Particular attention is paid to methods for limiting problem dimensionality. The approach is presented through three illustrative case studies, and its benefits and range of applicability are discussed.