Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios

Xinbin Liang, Xu Zhu, Siliang Chen, Xinqiao Jin, Fu Xiao and Zhimin Du

Applied Energy, 2023, vol. 349, issue C, No S0306261923010061

Abstract: Smart management of building energy devices, including their optimal control and fault detection technology, is of great significance to building energy conservation. The core of smart management is the development of reference models for target energy device. However, existing reference models show poor extrapolation ability when the operation conditions of online data are outside the scope of training data. To tackle this problem, here we propose a novel physics-constrained cooperative learning framework to train multiple reference models in a cooperative manner in order to improve their extrapolation ability. The general idea of cooperative learning is to constrain the output of different reference models on unknown operation conditions such that the physical inconsistent loss is minimized. In this study, two novel physical inconsistent losses, including energy conservation inconsistent loss and mass conservation inconsistent loss, are designed for seven output reference variables of chiller, forming the physics-constrained cooperative neural networks (PCNNs). Comprehensive data experiments are conducted to compare the model performance of PCNNs with other machine learning models, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). The experimental results showed that the PCNNs outperformed the other models under extrapolation scenarios, showing a lager performance improvement of mean absolute error (MAE) and root mean squared error (RMSE) metrics with 26.94% and 23.49%, respectively. The proposed physics-constrained cooperative learning framework might provide a new perspective for the development of reference models in building energy system.

Keywords: Physics-constrained cooperative learning; Building energy systems; Deep learning; Smart management; Extrapolation ability (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923010061
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:349:y:2023:i:c:s0306261923010061

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.121642

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().