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We evaluate ensemble models, namely, random forests, on data collected from an 8 \u00d7 8 PIR matrix thermopile sensor with the dual goal of predicting individual cell temperature values and subsequently detecting the occupancy status. Evaluation of the method is based on a real case study deployed in an IT Hub in Bucharest, for which we have collected over three weeks of ground data, analyzed, and used it in order to predict occupancy in a room. Results show a 2\u20134% mean absolute percentage error for the temperature prediction and &gt;99% accuracy for a three\u2010class model to detect human presence. The resulting outputs can be used by predictive building control models to optimize the commands to various subsystems. By separating the specific deployment from the system architecture and data structure, the application can be easily translated to other usage profiles and built environment entities. As compared to vision\u2010based systems, our solution preserves privacy with improved performance when compared to single PIR or indirect estimation.<\/jats:p>","DOI":"10.1155\/2021\/8000595","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T16:39:17Z","timestamp":1634834357000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Privacy\u2010Preserving Sensing and Two\u2010Stage Building Occupancy Prediction Using Random Forest Learning"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9647-6817","authenticated-orcid":false,"given":"Grigore","family":"Stamatescu","sequence":"first","affiliation":[]},{"given":"Claudia","family":"Chitu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"e_1_2_11_1_2","unstructured":"HartleyM. D.andMcCabeJ. 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