Description

Model predictive control (MPC) has become popular in buildings in recent years due to its potential to save HVAC operation energy and improve thermal comfort. One type of MPC for HVAC systems is EnergyPlus Model-based Predictive Control (EPMPC), where an EnergyPlus model is integrated into a MPC algorithm to predict future building energy performance. EPMPC could reduce the effort of developing a MPC algorithm by reusing the EnergyPlus model that is commonly developed during the design phase of a building project. However, MPC, especially EPMPC, is more complex and computationally-intensive compared to traditional rule-based HVAC control logic. It also needs to constantly acquire updated forecast data as inputs for computation, such as weather forecast data and occupancy schedule forecast data. Therefore, implementation of MPC to real HVAC systems operation is challenging. In this study, a software framework of MPC for HVAC systems was developed to facilitate its implementation. EPMPC was deployed in the Center for Sustainable Landscape building (CSL) in Pittsburgh, PA by using the framework as a case study. The framework is constructed using a client-server structure. In this structure, a light client program runs in the local computer connected to the building automation system (BAS) to write control values from MPC computation to HVAC systems, while a heavier server program can run in a remote computer to conduct intensive MPC computation. Hence, the computation-intensive work of MPC is hidden behind the scene and MPC becomes a simple “plug-in” algorithm for BAS. Through providing software interfaces in the server program, the framework also decouples MPC algorithm from forecast models that is used to provide inputs for MPC computation. This study demonstrated that, by using the framework, an EPMPC algorithm can be successfully implemented in the CSL building’s HVAC systems without major changes to the building’s existing BAS. The EPMPC algorithm is also evaluated and the practical issues, such as scalability, flexibility and HVAC controllability, are discussed.

 

Citation: 2017 Annual Conference, Long Beach, CA, Conference Papers

Product Details

Published:

2017

Number of Pages:

8

Units of Measure:

Dual

File Size:

1 file , 1.1 MB

Product Code(s):

D-LB-17-C054