Minimization of electricity demand and cost for multi-zone buildings: Part I—Modeling and validation

Abstract

Heating, ventilation, and air-conditioning systems are complex in terms of components that make them up and their different time scales. The inefficient operation of a heating, ventilation, and air-conditioning system leads to unreasonable electricity consumption during peak periods, which is accompanied by a high cost of electricity use. In a multi-zone building, multiple thermal interactions among the different thermal zones and the effects on electricity demand and cost are not well understood, due to the lack of fundamental knowledge. Meanwhile, multi-zone interactions and building dynamics play a crucial role in the overall electricity demand, cost, and load profiles due to the dependency of states of each individual zone on the thermal characteristics and states of the adjacent zones. The objective of this research is to understand multi-zone and equipment interactions in buildings energy systems, and to use that knowledge to minimize electricity demand and cost. To the best of the authors' knowledge, this is the first research to integrate building dynamics into controller formulation and design through the use of a physically representative thermal model that captures important phenomenon of building load and cooling coil operations. The current article is laid out in two parts. This first part introduces the development and validation of transient thermal models for building load and cooling coil operations in an actual air-handling unit serving a multi-zone office building. It also introduces a simplified model for supply fan power and speed. The models were developed using fundamental heat transfer equations and fan laws, with the model parameters estimated from short-period measurement data. The respective models are validated using actual data from building automation system. The results are presented, and they show high accuracy for building load, cooling coil, and the fan model, such that suitable predictive control methods could be used to minimize the overall electricity demand and cost. The minimization framework and optimization results are discussed in a subsequent article.