Buildings account for about 41% of primary energy consumption and 75% of the electricity. Space heating, space cooling, and ventilation are the dominant end uses, accounting for 41% of all energy consumed in the buildings sector. Growing interest in sustainability has resulted in research efforts to reduce energy consumption while providing adequate comfort to users.In this work, we present a Model Predictive Control (MPC) framework for optimal HVAC control that minimizes energy consumption while staying within the comfort bounds of the occupants. The novelty of our approach lies in the use of prediction occupancy models derived from data traces and incorporating those models within the MPC framework. We use a Blended Markov Chain (BMC) occupancy prediction model in order to predict thermal load and occupancy of each zone in the building. We test our approach in simulation and compare it with occupancy schedules and control rules currently use in our university buildings. Our preliminary results show that 15.5% savings in cooling in the summer, and 9.4% savings in heating in the winter are achievable when conditioning the building using our MPC/BMC control framework.

In order to achieve sustainability, steps must be taken to reduce energy consumption. In particular, heating, cooling, and ventilation systems, which account for 42% of the energy consumed by US buildings in 2010 [8], must be made more efficient. In this paper, we demonstrate ThermoSense, a new system for estimating occupancy. Using this system we are able to condition rooms based on usage. Rather than fully conditioning empty or partially filled spaces, we can control ventilation based on near real-time estimates of occupancy and temperature using conditioning schedules learned from occupant usage patterns. ThermoSense uses a novel multisensor node that utilizes a low-cost, low-power thermal sensor array along with a passive infrared sensor. By using a novel processing pipeline and sensor fusion, we show that our system is able measure occupancy with a RMSE of only ≈0.35 persons. By conditioning spaces based on occupancy, we show that we can save 25% energy annually while maintaining room temperature effectiveness.

Humans spend 90% of their lives inside buildings, but often the Heating, Ventilation, and Air Conditioning (HVAC) systems of commercial buildings do not properly maintain occupant comfort. Use of feedback through comfort voting applications has been shown to improve the quality of service, but the effects of application feedback and user interface design has not been investigated. In this work, we present several methods of feedback that use data presentation and environmental interaction in comfort voting applications. Through a 40 week user study of 61 University employees across 3 buildings, we show that feedback systems can be used to increase user satisfaction with thermal conditions from 33.9% to 93.3% and reduce energy consumption up to 18.99% compared to a system without voting. In addition, we find that by including a drifting control strategy, we find energy savings up to 37% can be realized without a significant reduction in satisfaction.

Objective: A significant proportion of inpatient antimicrobial prescriptions are inappropriate. Post-prescription review with feedback has been shown to be an effective means of reducing inappropriate antimicrobial use. However, implementation is resource intensive. Our aim was to evaluate the performance of traditional statistical models and machine-learning models designed to predict which patients receiving broad-spectrum antibiotics require a stewardship intervention. Methods: We performed a single-center retrospective cohort study of inpatients who received an antimicrobial tracked by the antimicrobial stewardship program. Data were extracted from the electronic medical record and were used to develop logistic regression and boosted-tree models to predict whether antibiotic therapy required stewardship intervention on any given day as compared to the criterion standard of note left by the antimicrobial stewardship team in the patient’s chart. We measured the performance of these models using area under the receiver operating characteristic curves (AUROC), and we evaluated it using a hold-out validation cohort. Results: Both the logistic regression and boosted-tree models demonstrated fair discriminatory power with AUROCs of 0.73 (95% confidence interval [CI], 0.69–0.77) and 0.75 (95% CI, 0.72–0.79), respectively (P = .07). Both models demonstrated good calibration. The number of patients that would need to be reviewed to identify 1 patient who required stewardship intervention was high for both models (41.7–45.5 for models tuned to a sensitivity of 85%). Conclusions: Complex models can be developed to predict which patients require a stewardship intervention. However, further work is required to develop models with adequate discriminatory power to be applicable to real-world antimicrobial stewardship practice.

To our knowledge, this is the first case report using dalbavancin in clinical practice for the treatment of MSSA bacteremia secondary to septic phlebitis. This report highlights the potential role of the newer lipoglycopeptides, such as dalbavancin, in treating patients who require long-term parenteral antimicrobial therapy and are ineligible for treatment via OPAT.