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• W2769673999 abstract "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." @default.
• W2769673999 created "2017-12-04" @default.
• W2769673999 creator A5035004860 @default.
• W2769673999 creator A5062049138 @default.
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• W2769673999 date "2017-11-30" @default.
• W2769673999 modified "2023-10-14" @default.
• W2769673999 title "Blackbox modeling of central heating and cooling plant equipment performance" @default.
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• W2769673999 doi "https://doi.org/10.1080/23744731.2017.1401417" @default.
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