This study aims to identify critical parameters of HVAC systems that drive the changes in the building energy-use profiles and develop an automated approach for identifying HVAC operational signatures that result in certain energy profiles in buildings, OperAtional Signature Identification System (OASIS). The OASIS relies on data-driven methodologies and is composed of three major steps: data preprocessing, feature selection, and signature discovery and analysis. The approach was tested on several air handling units (AHUs). The results showed that it is possible to define operational signatures for facility operators that are specific to a given building for running AHUs at these custom settings that correspond to energy efficient consumption in buildings.
Dedemen, G., and Ergan, S. (2018). “Quantifying performance degradation of HVAC systems for proactive maintenance using a data-driven approach.” 25th International Workshop on Intelligent Computing in Engineering (EG-ICE), June 10-13, 2018, Lausanne, Switzerland. DOI: https://link.springer.com/chapter/10.1007/978-3-319-91635-4_25.
Dedemen, G., Vakilinezhad, M. and Ergan, S. (2017). “Using data driven methodologies to identify patterns in BAS data to support facility operations.” International Workshop of Computing in Civil Engineering, June 25-27, 2017, Seattle, WA, pp. 282-289. DOI: https://doi.org/10.1061/9780784480823.034.
Dedemen, G., and Ergan, S. (2017). “Towards energy efficient operational patterns in Air Handling Units in highly sensed buildings.” 24th International Workshop on Intelligent Computing in Engineering -EG-ICE (2017), July 10-12, 2017, Nottingham, UK, pp. 45-54. DOI: https://www.nottingham.ac.uk/conference/fac-eng/eg-ice2017.
