<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Olivier Van Cutsem</style></author><author><style face="normal" font="default" size="100%">Maher Kayal</style></author><author><style face="normal" font="default" size="100%">David Blum</style></author><author><style face="normal" font="default" size="100%">Marco Pritoni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE PowerTech Milan 2019</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">commercial building</style></keyword><keyword><style  face="normal" font="default" size="100%">demand charge</style></keyword><keyword><style  face="normal" font="default" size="100%">Model predictive control (MPC)</style></keyword><keyword><style  face="normal" font="default" size="100%">peak demand</style></keyword><keyword><style  face="normal" font="default" size="100%">time-of-use tarrif</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Most medium and large commercial buildings in&amp;nbsp;the U.S. are subject to complex electricity tariffs that combine&amp;nbsp;both Time-of-Use (TOU) energy and demand charges. This study&amp;nbsp;analyses the performances of different economic Model Predictive&amp;nbsp;Control (MPC) formulations, from the standpoints of monthly bill&amp;nbsp;reduction, load shifting, and peak demand reduction. Simulations&amp;nbsp;are performed on many simplified commercial building models,&amp;nbsp;with multiple TOU demand charges, and under various summer&amp;nbsp;conditions. Results show that compared to energy-only MPC, the&amp;nbsp;traditional method for dealing with demand charges significantly&lt;br /&gt;reduces peak demand and owner bill, however, highlight a lack&amp;nbsp;of load shifting capability. A proposed incremental approach&lt;br /&gt;is presented, which better balances the bill components in the&amp;nbsp;objective function. In the case study presented, this method&lt;br /&gt;can improve monthly bill savings and increase load shifting&amp;nbsp;during demand response events, while keeping a similarly low&lt;br /&gt;peak demand, compared to traditional MPC methods taking into&amp;nbsp;account demand charges.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhe Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Marco Pritoni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferring occupant counts from Wi-Fi data in buildings through machine learning</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Building and Environment</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building control</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupancy estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupant count</style></keyword><keyword><style  face="normal" font="default" size="100%">Random forest</style></keyword><keyword><style  face="normal" font="default" size="100%">Wi-Fi data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0360132319303336</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">158</style></volume><pages><style face="normal" font="default" size="100%">281 - 294</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;An important approach to curtail building energy consumption is to optimize building control based on occupancy information. Various studies proposed to estimate occupant counts through different approaches and sensors. However, high cost and privacy concerns remain as major barriers, restricting the practice of occupant count detection. In this study, we propose a novel method utilizing data from widely deployed Wi-Fi infrastructure to infer occupant counts through machine learning. Compared with the current indirect measurement methods, our method improves the performance of estimating people count: (1) we avoid privacy concerns by anonymizing and reshuffling the MAC addresses on a daily basis; (2) we adopted a heuristic feature engineer approach to cluster connected devices into different types based on their daily connection duration. We tested the method in an office building located in California. In an area with an average occupancy of 22–27 people and a peak occupancy of 48–74 people, the root square mean error on the test set is less than four people. The error is within two people counts for more than 70% of estimations, and less than six counts for more than 90% of estimations, indicating a relatively high accuracy. The major contribution of this study is proposing a novel and accurate approach to detect occupant counts in a non-intrusive way, i.e., utilizing existing Wi-Fi infrastructure in buildings without requiring the installation of extra hardware or sensors. The method we proposed is generic and could be applied to other commercial buildings to infer occupant counts for energy efficient building control.&lt;/p&gt;</style></abstract></record></records></xml>