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
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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Energy and buildings. - 301 (2023) , 113694, ISSN: 0378-7788
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
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Universität
- (wann)
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2023
- Urheber
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Papadopoulos, Dimitrios
Dadras Javan, Farzad
Najafi, Behzad
Haghighat Mamaghani, Alireza
Rinaldi, Fabio
- DOI
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10.1016/j.enbuild.2023.113694
- URN
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urn:nbn:de:bsz:25-freidok-2408509
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:35 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Papadopoulos, Dimitrios
- Dadras Javan, Farzad
- Najafi, Behzad
- Haghighat Mamaghani, Alireza
- Rinaldi, Fabio
- Universität
Entstanden
- 2023
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