A State-Space Identification of Building Temperature System

This paper considers an estimation of state-space system matrices of a building temperature system. The building model has two rooms equipped with air-conditioning (AC) system where they contain heat interactions with walking path and the outdoor. Only one-dimensional heat transfer and internal energy change have been concerned while neglecting the effects caused by humidity and air leakage. Cooling energy from the AC system is expressed via the coefficient of performance (COP) and is a nonlinear function of room temperatures. We propose an approximate linear model with an assumption that the COP is a constant obtained by the air-conditioner specifications or it can be estimated. Temperature data and air-conditioners electrical input data can be collected by the Chulalongkorn University Building Energy Management System (CUBEMS) and therefore allow us to estimate the model parameters using the least-squares approach. Our results show that the model with a structure on the input matrix that agrees with physically-based dynamical equations is recommended and including the solar radiation as an input is not necessary.

I. Introduction

Heat dynamics of building temperature system can be traditionally derived from the principles of heat conduction, convection, radiation and simplified to a heat transfer model which requires the knowledge of physical characteristics of materials used in the building. Typical variables in the dynamics are room (or indoor) temperatures, outdoor temperature, solar radiation, heating or cooling devices in the room [1], [2], [3]. Simplified thermal dynamics in a room can be described by a first-order differential equation which is equivalent to a thermal RC (resistance/capacitance) network. Knowing dynamical models of the system allows us to analyze the system characteristics, predict room temperatures at a certain time, and also provide a plant model for engineers to control operations for the heating or cooling devices. For these reasons, estimation of model parameters of the building energy system has become a topic of interest in many studies [4].