LB-17-C054 -- An Implementation Framework of Model Predictive Control for HVAC Systems: A Case Study of EnergyPlus Model-Based Predictive Control PDF

LB-17-C054 -- An Implementation Framework of Model Predictive Control for HVAC Systems: A Case Study of EnergyPlus Model-Based Predictive Control PDF

Name:
LB-17-C054 -- An Implementation Framework of Model Predictive Control for HVAC Systems: A Case Study of EnergyPlus Model-Based Predictive Control PDF

Published Date:
2017

Status:
Active

Description:

Publisher:
ASHRAE

Document status:
Active

Format:
Electronic (PDF)

Delivery time:
10 minutes

Delivery time (for Russian version):
200 business days

SKU:

Choose Document Language:
$4.8
Need Help?

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.

 


File Size : 1 file , 1.1 MB
Note : This product is unavailable in Russia, Belarus
Number of Pages : 8
Product Code(s) : D-LB-17-C054
Published : 2017
Units of Measure : Dual

History


Related products


Best-Selling Products

TAPPI 0109DIRT
Published Date:
Dirt Estimation Chart (Opaque)
$34.5
TAPPI 0109DIRTCAL
Published Date:
Calibrated Size Estimation Chart
TAPPI 0109DIRTT
Published Date:
Size Estimation Chart (Transparency)
$25.2
TAPPI T 1006 sp-06
Published Date: 01/17/2006
Testing of Fiber Glass Mats: Use of Modified TAPPI Prodecures for Sampling and Lot Acceptance, Stiffness, Tear Resistance, Air Permeability, and Thickness
TAPPI T 1006 sp-10
Published Date: 01/01/2010
Testing of Fiber Glass Mats: Use of Modified TAPPI Prodecures for Sampling and Lot Acceptance, Stiffness, Tear Resistance, Air Permeability, and Thickness
$14.7
TAPPI T 1006 sp-15 (R2022)
Published Date: 2014
Testing of Fiber Glass Mats: Use of Modified TAPPI Prodecures for Sampling and Lot Acceptance, Stiffness, Tear Resistance, Air Permeability, and Thickness
$14.7