Blackbox modeling of central heating and cooling plant equipment performance

• Published in: HVAC&R research Vol. 24; no. 4; pp. 396 - 409
• Main Authors: Gunay, H. Burak, Shen, Weiming, Yang, Chunsheng
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 21.04.2018; Taylor & Francis Ltd
• Subjects: Artificial neural networks; Black boxes; Boilers; Central heating; Chillers; Flow velocity; Flue gas; Mathematical models; Neural networks; Regression analysis; Water flow; Water temperature
• ISSN: 2374-4731; 2374-474X
• DOI: 10.1080/23744731.2017.1401417

The current article presents an analysis conducted upon the sensor data gathered from the distribution control system of a central heating and cooling plant in Ottawa, Canada. After observing that the performance of four boilers and five chillers of this plant vary substantially in time under steady-state conditions, data-driven models were developed to explain this variability from the archived sensor data. By employing a forward stepwise regression and a repeated random sub-sampling cross-validation approach, two-layer feed-forward artificial neural network models with 7 to 15 hidden-nodes were selected for each boiler and chiller. The selected boiler models could explain 84% to 95% of the variability in a boiler's efficiency, and the selected chiller models could explain 65% to 94% of the variability in a chiller's coefficient of performance. Among studied nine variables, the most informative ones to predict a boiler's efficiency were identified as follows: flue gas O 2 concentration, pressure, part-load ratio, forced draft fan state, and return water flow rate. Unlike boilers, all four studied variables were found useful in predicting a chiller's coefficient of performance. These four variables were the return water flow rate, part-load ratio, outdoor temperature, and return water temperature. A residual analysis was conducted to verify the appropriateness of the selected models to the datasets. In addition, potential use cases for the selected models were discussed with illustrative examples.