Blackbox modeling of the central heating and cooling plant equipment performance

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DOIResolve DOI: http://doi.org/10.1080/23744731.2017.1401417
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TypeArticle
Journal titleScience and Technology for the Built Environment
ISSN2374-4731
2374-474X
Subjectblackbox model; central heating and cooling plants; artificial neural network; model selection
AbstractThis paper 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 seven to fifteen 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.
Publication date
PublisherTaylor & Francis
LanguageEnglish
AffiliationConstruction; Digital Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23002527
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Record identifier26d44fe4-e608-4e7e-b92b-a9bd9bea30ef
Record created2017-11-22
Record modified2017-11-22
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