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Open AccessFeature PaperArticle

Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components

1
Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH 45221, USA
2
Department of Architectural Engineering, Gachon University, Seongnam-si 1342, Korea
*
Author to whom correspondence should be addressed.
Buildings 2020, 10(9), 163; https://doi.org/10.3390/buildings10090163
Received: 31 July 2020 / Revised: 10 September 2020 / Accepted: 11 September 2020 / Published: 13 September 2020
Most of the energy consumed by the residential and commercial buildings in the U.S. is dedicated to space cooling and heating systems, according to the U.S. Energy Information Administration. Therefore, the need for better operation mechanisms of those existing systems become more crucial. The most vital factor for that is the need for accurate models that can accurately predict the system component performance. Therefore, this paper’s primary goal is to develop a new accurate data-driven modeling and optimization technique that can accurately predict the performance of the selected system components. Several data-enabled modeling techniques such as artificial neural networks (ANN), support vector machine (SVM), and aggregated bootstrapping (BSA) are investigated, and model improvements through model structure optimization proposed. The optimization algorithm will determine the optimal model structures and automate the process of the parametric study. The optimization problem is solved using a genetic algorithm (GA) to reduce the error between the simulated and actual data for the testing period. The models predicted the performance of the chilled water variable air volume (VAV) system’s main components of cooling coil and fan power as a function of multiple inputs. Additionally, the packaged DX system compressor modeled, and the compressor power was predicted. The testing results held a low coefficient of variation (CV%) values of 1.22% for the cooling coil, and for the fan model, it was found to be 9.04%. The testing results showed that the proposed modeling and optimization technique could accurately predict the system components’ performance. View Full-Text
Keywords: HVAC; cooling coil; fan; DX compressor; ANN; accurate data-driven models; optimization; GA; chilled water VAV system; packaged DX system HVAC; cooling coil; fan; DX compressor; ANN; accurate data-driven models; optimization; GA; chilled water VAV system; packaged DX system
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MDPI and ACS Style

Talib, R.; Nabil, N.; Choi, W. Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components. Buildings 2020, 10, 163. https://doi.org/10.3390/buildings10090163

AMA Style

Talib R, Nabil N, Choi W. Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components. Buildings. 2020; 10(9):163. https://doi.org/10.3390/buildings10090163

Chicago/Turabian Style

Talib, Rand; Nabil, Nassif; Choi, Wonchang. 2020. "Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components" Buildings 10, no. 9: 163. https://doi.org/10.3390/buildings10090163

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