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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.
Nomenclature
| Cin | = | thermal capacitance of room air (J/K) |
| Cint | = | thermal capacitance of internal mass (J/K) |
| Cp | = | thermal capacitance of internal wall or partition (J/K) |
| Cw | = | thermal capacitance of exterior wall (J/K) |
| hchw | = | chilled water enthalpy ( |
| k | = | thermal conductivity (W/mK) |
| N | = | fan speed (%) |
| Q | = | airflow rate (m3/s) |
| Qconv | = | convective part of internal load (W) |
| Qr | = | half of the sum of radiative components from internal load and windows (W) |
| Qsys | = | system extraction or heating rate (W) |
| Re | = | thermal resistance of external wall (m2K/W) |
| Rint | = | thermal resistance of internal mass (m2K/W) |
| Rwin | = | windows resistance (m2K/W) |
| Tair | = | air temperature ( |
| Tamb | = | ambient temperature ( |
| Tchw | = | chilled water temperature ( |
| TiE | = | Inside surface temperature of east wall (°C) |
| Tin | = | room temperature (°C) |
| Tint | = | internal mass temperature ( |
| ToE | = | outside surface temperature of east wall (°C) |
| TsE | = | sol-air temperature of East facing wall (°C) |
| W | = | fan power (kW) |
| X | = | thermal resistance (m2K/W) and capacitances (J/K) |
Funding
This study was funded by ASHRAE Grant-in-Aid, as part of the dissertation titled “Investigation of methodologies for minimizing buildings electricity demand and cost.” The authors would like to thank ASHRAE for the sponsorship.