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However, instrumenting every electrical panel in a large commercial building is an expensive proposition. In this article, we demonstrate that it is also unnecessary. Specifically, we propose a greedy meter (sensor) placement algorithm based on maximization of information gain subject to a cost constraint. The algorithm provides a near-optimal solution guarantee, and our empirical results demonstrate a 15% improvement in prediction power over conventional methods. Next, to identify power-saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Furthermore, to enable a building manager to effectively plan for demand response programs, we evaluate several solutions for fine-grained, short-term load forecasting. Our investigation reveals that support vector regression and an ensemble model work best overall. Finally, to better manage resources such as lighting and HVAC, we propose a semisupervised approach combining hidden Markov models (HMMs) and a standard classifier to model occupancy based on readily available port-level network statistics. We show that the proposed two-step approach simplifies the occupancy model while achieving good accuracy. The experimental results demonstrate an average occupancy estimation error of 9.3% with a potential reduction of 9.5% in lighting load using our occupancy models.<\/jats:p>","DOI":"10.1145\/3110219","type":"journal-article","created":{"date-parts":[[2017,8,29]],"date-time":"2017-08-29T17:49:18Z","timestamp":1504028958000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Analytics for Managing Power in Commercial Buildings"],"prefix":"10.1145","volume":"1","author":[{"given":"Gowtham","family":"Bellala","sequence":"first","affiliation":[{"name":"C3 IoT, Redwood City, CA"}]},{"given":"Manish","family":"Marwah","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs, Palo Alto, CA"}]},{"given":"Martin","family":"Arlitt","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs, Palo Alto, CA"}]},{"given":"Geoff","family":"Lyon","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs, Palo Alto, CA"}]},{"given":"Cullen","family":"Bash","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs, Palo Alto, CA"}]},{"given":"Amip","family":"Shah","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs, Palo Alto, CA"}]}],"member":"320","published-online":{"date-parts":[[2017,8,29]]},"reference":[{"volume-title":"10th International Conference on Information Processing in Sensor Networks (IPSN\u201911)","author":"Agarwal Y.","key":"e_1_2_1_1_1","unstructured":"Y. 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