Garay Martínez, RobertoSiddique, Muhammad TalhaLópez Garde, Juan Manuel2025-06-042025-06-042024Garay-Martinez, R., Siddique, M. T., & Lopez-Garde, J. M. (2024). Model-based outlier detection in district heating systems. Procedia Computer Science, 246(C), 2071-2079. https://doi.org/10.1016/J.PROCS.2024.09.64610.1016/J.PROCS.2024.09.646https://hdl.handle.net/20.500.14454/2927Ponencia presentada en la 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024), celebrada en Sevilla, entre el 11 y el 13 de septiembre de 2024Data-driven methods are increasingly popular for building energy performance assessment. For these to be useful, it is required that good quality data is created through the filtering of outliers and imputation of missing information. In the field of energy use in buildings, there is a clear sensitivity of heat load to outdoor climate, which needs to be considered when identifying outliers and developing imputation methods. We propose to use a well-known changepoint model to define the sensitivity of the data to climate, further segmented by time of the week. Then we use the residuals of the model to identify outliers, where those observation with residuals substantially out of the normality expectations are identified as outliers. Then missing data is repaired by means of linear imputation techniques, considering the patterns for same times of the week in the dataset. As a result of the full process, we were able to identify 5% of outliers, which resulted in the improvement of model metrics in the range of 20% mean absolute error (MAE) and slightly better R2 values.eng© 2024 The AuthorsBuilding energy performanceData-driven modelOutlier detectionconference paper2025-06-041877-0509