Assessment of different data-driven algorithms for ahu energy consumption predictions

Fuxin Niu, Zheng O'Neill, Wangda Zuo, Yanfei Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

including AutoRegressive with eXternal inputs (ARX), State Space (SS), Subspace state space (N4S) and Bayesian Network (BN) are evaluated and compared using a case study of predictions of Air Handler Unit (AHU) thermal energy consumption. Training and testing data are generated from a dynamic Modelica-based AHU model. Four evaluation metrics of Root Mean Squared Error (RMSE), coefficient of determination (R2), Normalized Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Square Error (CVRMSE) are used to compare the model prediction performance of different algorithms. The best algorithm is selected and proposed following the criteria recommonded by ASHRAE Guideline 14. Using the proposed data driven algorithm, the relation of AHU energy consumption with mixed air temperature, air flow rate, and supply water temperature are obtained. In the future, such correlations will be employed for an optimization analysis of AHU energy consumption.

Original languageEnglish (US)
Title of host publication14th International Conference of IBPSA - Building Simulation 2015, BS 2015, Conference Proceedings
PublisherInternational Building Performance Simulation Association
Pages1539-1546
Number of pages8
StatePublished - 2015
Event14th Conference of International Building Performance Simulation Association, BS 2015 - Hyderabad, India
Duration: Dec 7 2015Dec 9 2015

Other

Other14th Conference of International Building Performance Simulation Association, BS 2015
CountryIndia
CityHyderabad
Period12/7/1512/9/15

ASJC Scopus subject areas

  • Computer Science Applications
  • Architecture
  • Modeling and Simulation
  • Building and Construction

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