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BUILDING ENERGY CONSUMPTION ON-LINE FORECASTING USING SYSTEM IDENTIFICATION AND DATA FUSION
Conference proceeding

BUILDING ENERGY CONSUMPTION ON-LINE FORECASTING USING SYSTEM IDENTIFICATION AND DATA FUSION

Xiwang Li, Jin Wen and ASME
7TH ANNUAL DYNAMIC SYSTEMS AND CONTROL CONFERENCE, 2014, VOL 1, v 1, V001T07A002
01 Jan 2014

Abstract

Automation & Control Systems Engineering Engineering, Mechanical Operations Research & Management Science Science & Technology Technology
Model based control has been proven to have significant building energy saving potentials through operation optimization. Accurate and computationally efficient, and cost-effective building energy model are essential for model based control. Existing studies in this area have mostly been focusing on reducing computation burden using simplified physics based modeling approach. However, creating and identification the simplified physics based model is often challenging and requires significant engineering efforts. Therefore, this study proposes a novel methodology to develop building energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. System identification model has been developed based on frequency domain spectral density analysis. Eigensystem realization algorithm is used to generate the state space model from the Markov parameters. Kalman filter based data fusion technique has also been implemented to improve the accuracy and robustness of the model by incorporating with real measurements. A systematic analysis of system structure, system excitation selection as well as data fusion implementation is also demonstrated. The developed strategies are evaluated using a simulated testing building (simulated in EnergyPlus environment). The overall building energy estimation accuracy from this proposed model can reach to above 95% within 2 minutes calculation time, when compared against detailed physics based simulation results from the EnergyPlus model.

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3 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#13 Climate Action
#7 Affordable and Clean Energy
#11 Sustainable Cities and Communities

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Web of Science research areas
Automation & Control Systems
Engineering, Mechanical
Operations Research & Management Science
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