A Hybrid Physics-Based and Data Driven Approach to Optimal Control of Building Cooling/Heating Systems

S A Vaghefi,M A Jafari,J Brouwer,J Zhu,Y Lu

doi:10.1109/tase.2014.2356337

Abstract

This work integrates a physics-based model with a data driven time-series model to forecast and optimally manage building energy. Physical characterization of the building is partially captured by a collection of zonal energy balance equations with parameters estimated using a least squares estimation (LSE) technique and data initially generated from the EnergyPlus building model. A generalized Cochran–Orcutt estimation technique is adopted to describe the data generated from these simulations. The combined forecast model is then used in a model predictive control (MPC) framework to manage heating and cooling set points. This work is motivated by the practical limitations of simulation-based optimizations. Once the forecast model is established capturing sufficient statistical variability and physical behavior of the building, there will be no more need to run EnergyPlus in the optimization routine.

Highlights

I N THIS PAPER, we introduce a forecast model of a building cooling/heating system and use it in conjunction with an adaptive Model Predictive Control (MPC) algorithm to optimize building HVAC set points
The forecast combines a physics-based model of building zone energy balances with a Manuscript received March 06, 2014; revised July 01, 2014; accepted July 30, 2014
There are more variables such as occupancy and cooling fans power that can affect the total power consumption. These effects cannot be explained through the linear structure of (13), and as a result, they emerge into the error terms

Summary

INTRODUCTION

I N THIS PAPER, we introduce a forecast model of a building cooling/heating system and use it in conjunction with an adaptive Model Predictive Control (MPC) algorithm to optimize building HVAC set points. Most soft computing techniques cannot guarantee full capture of complex interactions amongst building components, dynamic variables, and cooling/heating load, especially when the available real data is limited and the building includes a complicated multi-zone structure. To overcome such problems, a number of researchers employ a simulation-based approach to capture the dynamic behavior of buildings and thereby optimize energy use [16]–[18]. A number of researchers employ a simulation-based approach to capture the dynamic behavior of buildings and thereby optimize energy use [16]–[18] In this approach, first, a highly granular physics-based simulation model of the building is developed. These approaches are similar to that developed and used

MODEL FRAMEWORK

ENERGY FORECAST MODEL

OPTIMAL CONTROL STRATEGY

NUMERICAL EXAMPLE

SUMMARY AND CONCLUSION