A considerable portion of total energy loss within the built environment originates from operational errors during the actual lifespan of a building. With the rise of fully automated commercial buildings, a large amount of sensory data is becoming available that can be leveraged to detect and predict such errors. However, processing these data on-site requires significant knowledge and effort by building op this work, a combination of model-based and data-driven approaches are employed to facilitate the analysis of historical energy demand data. Using change-point models and symbolic quantisation techniques, a large dataset of heating and cooling demand profiles several office buildings are transformed into a format that is easily interpreted by the building operator and is suitable for actionable anomaly detection. Further quantification of anomalies and calculation of potential savings are drawn from the results.

2019 ASHRAE Winter Conference
Department of Civil and Environmental Engineering

Ashouri, A. (Araz), Hu, Y. (Yitian), Gunay, H.B, Newsham, G.R. (Guy R.), & Shen, W. (Weiming). (2019). Model-based and data-driven anomaly detection for heating and cooling demands in office buildings. In ASHRAE Transactions (pp. 87–95).