{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:02:07Z","timestamp":1774292527734,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T00:00:00Z","timestamp":1603238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office","award":["DE-AC36-08GO28308"],"award-info":[{"award-number":["DE-AC36-08GO28308"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.<\/jats:p>","DOI":"10.3390\/s20205947","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T20:51:00Z","timestamp":1603399860000},"page":"5947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9884-5199","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"first","affiliation":[{"name":"National Renewable Energy Laboratory, Buildings and Thermal Sciences Center, Golden, CO 80401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"key":"ref_1","unstructured":"EIA (2020, July 01). Monthly Energy Review\u2014June 2020, Available online: https:\/\/www.eia.gov\/totalenergy\/data\/monthly\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.enbuild.2018.11.010","article-title":"A systematic feature selection procedure for short-term data-driven building energy forecasting model development","volume":"183","author":"Zhang","year":"2019","journal-title":"Energy Build."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gruenwald, L., Chok, H., and Aboukhamis, M. (2007, January 28\u201331). Using data mining to estimate missing sensor data. Proceedings of the Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), Omaha, NE, USA.","DOI":"10.1109\/ICDMW.2007.103"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.enbuild.2013.02.049","article-title":"Data association mining for identifying lighting energy waste patterns in educational institutes","volume":"62","author":"Cabrera","year":"2013","journal-title":"Energy Build."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1080\/01621459.1994.10476768","article-title":"Missing data, imputation, and the bootstrap","volume":"89","author":"Efron","year":"1994","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1076\/edre.7.4.353.8937","article-title":"A review of methods for missing data","volume":"7","author":"Pigott","year":"2001","journal-title":"Educ. Res. Eval."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1002\/sim.2787","article-title":"Multiple imputation: Review of theory, implementation and software","volume":"26","author":"Harel","year":"2007","journal-title":"Stat. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1198\/016214504000001844","article-title":"Missing-data methods for generalized linear models: A comparative review","volume":"100","author":"Ibrahim","year":"2005","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_9","unstructured":"Ekwevugbe, T., Brown, N., and Pakka, V. (2013, January 8\u201311). Realt-time building occupancy sensing for supporting demand driven hvac operations. Proceedings of the International Conference for Enhanced Building Operations (ICEBO), Montr\u00e9al, QC, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.enbuild.2014.02.005","article-title":"Data mining in building automation system for improving building operational performance","volume":"75","author":"Xiao","year":"2014","journal-title":"Energy Build."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.apenergy.2017.12.051","article-title":"Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks","volume":"212","author":"Rahman","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Peppanen, J., Zhang, X., Grijalva, S., and Reno, M.J. (2016, January 6\u20139). Handling bad or missing smart meter data through advanced data imputation. Proceedings of the 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Minneapolis, MN, USA.","DOI":"10.1109\/ISGT.2016.7781213"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109941","DOI":"10.1016\/j.enbuild.2020.109941","article-title":"A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data","volume":"216","author":"Ma","year":"2020","journal-title":"Energy Build."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1177\/0143624415573215","article-title":"A case study to examine the imputation of missing data to improve clustering analysis of building electrical demand","volume":"36","author":"Inman","year":"2015","journal-title":"Build. Serv. Eng. Res. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Schachinger, D., Gaida, S., Kastner, W., Petrushevski, F., Reinthaler, C., Sipetic, M., and Zucker, G. (2016, January 6\u20139). An advanced data analytics framework for energy efficiency in buildings. Proceedings of the 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany.","DOI":"10.1109\/ETFA.2016.7733630"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Habib, U., Zucker, G., Blochle, M., Judex, F., and Haase, J. (2015, January 9\u201312). Outliers detection method using clustering in buildings data. Proceedings of the IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, Japan.","DOI":"10.1109\/IECON.2015.7392181"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.enbuild.2014.04.031","article-title":"Comparison of building energy use data between the United States and China","volume":"78","author":"Xia","year":"2014","journal-title":"Energy Build."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Garnier, A., Eynard, J., Caussanel, M., and Grieu, S. (2012, January 6\u20138). Missing data estimation for energy resources management in tertiary buildings. Proceedings of the CCCA12, Marseilles, France.","DOI":"10.1109\/CCCA.2012.6417902"},{"key":"ref_19","unstructured":"Bradley, J.V. (1968). Distribution-Free Statistical Tests, Prentice-Hall. 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