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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 operators. In 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 collected from 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.

Citation: 2019 Winter Conference, Atlanta, GA, Conference Papers