Journal article
Comparison of On-line Building Energy Forecasting Model Using System Identification Method and Other Methods
ASHRAE transactions, Vol.121, pp.1W-1W
01 Jan 2015
Abstract
Buildings, which consume more than 70% of the electricity in the U.S., play significant roles in a smart-grid infrastructure. Currently, most of the existing energy forecasting models are in three different categories, namely white box models (detailed physics based models), black box models (purely data driven models) and grey box models (hybrid models), among which black box models and grey box models are commonly used in on-line building control. In this study, a set of model comparison criteria are proposed and used to evaluate the energy forecasting model generated from the reported system identification method and some other popular methods, including Resistance and Capacitance method, Support Vector Regression method, and Artificial Neural Networks method. A comprehensive EnergyPlus simulation model representing a mid-size commercial building is used to generate operating data for the model development and comparison in lieu of real building data. This study only focuses on cooling energy forecasting but the proposed method can be used for heating energy forecasting and whole building energy forecasting as well.
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Details
- Title
- Comparison of On-line Building Energy Forecasting Model Using System Identification Method and Other Methods
- Creators
- Xiwang Li - Drexel UniversityJin Wen - Drexel UniversityTeresa Wu - Arizona State University
- Publication Details
- ASHRAE transactions, Vol.121, pp.1W-1W
- Publisher
- American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Identifiers
- 991021960805204721