Handling complete short-term data logging failure in smart buildings: Machine learning based forecasting pipelines with sliding-window training scheme

Abstract: This paper implements a machine learning(ML)-based procedure for constructing the missing sensor(s) data in a net zero energy building in case of complete failure in data recording (for up to one hour). In the first scenario, missing temperature data is re-created using the sensor's ex-ante data, the HVAC system's status flag, and the ambient conditions. In the second scenario, the temperature data (until failure occurred) from two close-by spaces are also utilized as inputs. For each scenario, ML-based pipelines' performance is first assessed by considering different prediction horizons using a benchmark algorithm. Next, each pipeline's most promising features and the most suitable algorithm are identified. Using the obtained optimal pipeline, a sliding window-based training scheme is implemented, and the size of the training window is optimized. It is shown that feature selection, algorithm optimization procedures, and the sliding window-based training scheme notably improve the forecasting performance. The proposed methodology can be deployed as a tool in intervals with total data logging failure, providing data to ML-based controllers in smart buildings and avoiding disruptions in the building management system

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Energy and buildings. - 301 (2023) , 113694, ISSN: 0378-7788

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Freiburg
• (who): Universität
• (when): 2023
• Creator: Papadopoulos, Dimitrios ; Dadras Javan, Farzad ; Najafi, Behzad ; Haghighat Mamaghani, Alireza ; Rinaldi, Fabio

Further information

• DOI: 10.1016/j.enbuild.2023.113694
• URN: urn:nbn:de:bsz:25-freidok-2408509
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:35 AM CEST