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 O2 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.

Additional Metadata
Persistent URL dx.doi.org/10.1080/23744731.2017.1401417
Journal Science and Technology for the Built Environment
Citation
Gunay, H.B, Shen, W. (Weiming), & Yang, C. (Chunsheng). (2017). Blackbox modeling of central heating and cooling plant equipment performance. Science and Technology for the Built Environment, 1–14. doi:10.1080/23744731.2017.1401417