乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T04:41:07Z","timestamp":1721191267683},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"abstract":"Abstract<\/jats:title>The cooling systems contribute to 40% of overall building energy consumption. Out of which, 40% is wasted because of faulty parts that cause anomalies in the cooling systems. We propose a three-stage, non-invasive part-level anomaly detection technique to identify anomalies in both cooling systems, a ducted-centralized and a ductless-split. We use COTS sensors to monitor temperature and energy without invading the cooling system. After identifying the anomalies, we find the cause of the anomaly. Based on the anomaly, the solution recommends a fix. If there is a technical fault, our proposed technique informs the technician regarding the faulty part, reducing the cost and time needed to repair it. In the first stage, we propose a domain-inspired time-series statistical technique to identify anomalies in cooling systems. We observe an AUC-ROC<\/jats:italic> score of more than 0.93 in simulation and experimentation. In the second stage, we propose using a rule-based technique to identify the cause of the anomaly. We classify causes of anomalies into three classes. We observe an AUC-ROC<\/jats:italic> score of 1. Based on the anomaly classification, we identify the faulty part of the cooling system in the third stage. We use the Nearest-Neighbour Density-Based Spatial Clustering of Applications with Noise (NN-DBSCAN) algorithm with transfer learning capabilities to train the model only once, where it learns the domain knowledge using the simulated data. The trained model is used in different environmental scenarios with both types of cooling systems. The proposed algorithm shows an accuracy<\/jats:italic> score of 0.82 in simulation deployment and 0.88 in experimentation. In the simulation we used both ducted-centralized and ductless-split cooling systems and in the experimentation we evaluated the solution with ductless-split cooling systems. The overall accuracy<\/jats:italic> of the three-stage technique is 0.82 and 0.86 in simulation and experimentation, respectively. We observe energy savings of up to 68% in simulation and 42% during experimentation, with a reduction of ten days in the cooling system\u2019s downtime and up to 75% in repair cost.<\/jats:p>","DOI":"10.1186\/s42162-024-00351-1","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T10:09:05Z","timestamp":1718618945000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Detecting faults in the cooling systems by monitoring temperature and energy"],"prefix":"10.1186","volume":"7","author":[{"given":"Keshav","family":"Kaushik","sequence":"first","affiliation":[]},{"given":"Vinayak","family":"Naik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"351_CR1","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.enbuild.2017.02.058","volume":"144","author":"DB Araya","year":"2017","unstructured":"Araya DB, Grolinger K, ElYamany HF, Capretz MAM, Bitsuamlak G (2017) An ensemble learning framework for anomaly detection in building energy consumption. 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