Abstract

Ongoing commissioning of buildings is used for the analysis the energy performance and operation of the heating ventilating and air conditioning (HVAC) systems, based on the measurements of physical variables in an existing building. Prediction of heating energy demand, detection of abnormal energy performance and operation conditions, identifications of variables that affect the normal operation and performance are the goals of ongoing commissioning of buildings, as covered in this thesis.
This thesis proposes the development of benchmarking models to be used for the ongoing commissioning of the energy performance of heating system in two semi-detached houses of Inuvik, NWT, Canada. The scope is the comparison of the recorded measurements with the benchmarking models` predictions to detect changes in the energy performance. This is the first step in the ongoing commissioning, which is normally followed up by the identification of causes of such a change. This study compares the quality of predictions when the benchmarking model uses the static and augmented window techniques for retraining. On the average, over a longer prediction time interval, the measurements of total heating energy demand are close with the predictions of the benchmarking model that uses the static window technique. When the benchmarking models are retrained by using the augmented window technique, their predictions are useful for the comparison with measurements over shorter time intervals. The comparison between measurements and predictions as well as the analysis of information extracted from the daily signature of heating energy demand reveal more significant changes in the operation of heating system of house A compared with house B.
Another section of this thesis presents the application of the Principal Component Analysis (PCA) for the definition of the threshold of normal operation of the heating system in two houses, the detection of outliers in the PC-based space of the heating system operation, and the identification of those system variables which are the source of outliers. This case study uses measurements collected in December 2014 as the training data set, which is then applied to measurements of February 2015 as the application data set. The temperature of supply and return water temperature for heating one house are the major sources of outliers identified from data of February 2015. The identification by the PCA of variables with abnormal values is validated by using of a modified data set.