01863nas a2200205 4500008004100000245009300041210006900134260000900203520116400212653002401376653001801400653003501418653001601453653002301469100002401492700001701516700001601533700001901549856008901568 2019 eng d00aComparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs0 aComparison of MPC Formulations for Building Control under Commer c20193 a

Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formulations, from the standpoints of monthly bill reduction, load shifting, and peak demand reduction. Simulations are performed on many simplified commercial building models, with multiple TOU demand charges, and under various summer conditions. Results show that compared to energy-only MPC, the traditional method for dealing with demand charges significantly
reduces peak demand and owner bill, however, highlight a lack of load shifting capability. A proposed incremental approach
is presented, which better balances the bill components in the objective function. In the case study presented, this method
can improve monthly bill savings and increase load shifting during demand response events, while keeping a similarly low
peak demand, compared to traditional MPC methods taking into account demand charges.

10acommercial building10ademand charge10aModel predictive control (MPC)10apeak demand10atime-of-use tarrif1 aVan Cutsem, Olivier1 aKayal, Maher1 aBlum, David1 aPritoni, Marco uhttps://simulationresearch.lbl.gov/publications/comparison-mpc-formulations-building02420nas a2200253 4500008004100000022001300041245008400054210006900138260001200207300001400219490000800233520166500241653002101906653002101927653002501948653001901973653001801992653001502010100001402025700001902039700002202058700001902080856006702099 2019 eng d a0360132300aInferring occupant counts from Wi-Fi data in buildings through machine learning0 aInferring occupant counts from WiFi data in buildings through ma c05/2019 a281 - 2940 v1583 a

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.

10aBuilding control10aMachine learning10aOccupancy estimation10aOccupant count10aRandom forest10aWi-Fi data1 aWang, Zhe1 aHong, Tianzhen1 aPiette, Mary, Ann1 aPritoni, Marco uhttps://linkinghub.elsevier.com/retrieve/pii/S0360132319303336