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.
| File Size : | 1
file
, 1.5 MB |
| Note : | This product is unavailable in Russia, Belarus |
| Number of Pages : | 9 |
| Product Code(s) : | D-AT-19-C011 |
| Published : | 2019 |
| Units of Measure : | Dual |
