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[Display omitted] • Zone-level ANN model is established to predict load and energy consumption of HVAC. • Building energy flexibility is activated in a time-of-use electricity price scenario. • Rule-based control is adopted to optimize HVAC operation and conserve operation cost. • Operation cost is mitigated by 39.22% and 44.41% in the heating and cooling season. Precise and quick thermal load prediction for buildings is imperative in realizing the flexibility of building energy systems. Operation optimization based on the prediction results can effectively mitigate the energy consumption and operation cost. Here, an artificial neural network (ANN) model is built to predict the load demand and energy consumption of an office building. Thermal zones division is considered to improve the prediction accuracy. Measured data are collected to establish and validate the ANN model. The total number of samples for training, validation and testing are 13674, 3978 and 3977, respectively. A rule-based control optimization model is introduced and combined with ANN model to optimize the operation of heating, ventilation and air-conditioning system. Building energy flexibility is activated by optimization model in a time-of-use electricity price scenario. The results indicate that ANN model has high precision and the coefficient of variation of root mean squared errors corresponding to the load demand prediction and energy consumption prediction are 10.76% and 15.59%, respectively. Furthermore, the optimization results can reduce the operation cost by 39.22% and 44.41% in the heating and cooling season, respectively. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO 2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community. [Display omitted] • An IES with multiple renewable energy and energy storage subsystems is proposed. • A two-layer co-optimization model of IES design and operation is developed. • IES optimization results for building-level and community-level are compared. • Impact of roof area limitation for PV on IES optimization results is investigated. [ABSTRACT FROM AUTHOR]

The effect of COVID-19 infection on post-operative mortality and the optimal timing to perform ambulatory surgery from diagnosis date remains unclear in this population. Our study was to determine whether a history of COVID-19 diagnosis leads to a higher risk of all-cause mortality following ambulatory surgery. This cohort constitutes retrospective data obtained from the Optum dataset containing 44,976 US adults who were tested for COVID-19 up to 6 months before surgery and underwent ambulatory surgery between March 2020 to March 2021. The primary outcome was the risk of all-cause mortality between the COVID-19 positive and negative patients grouped according to the time interval from COVID-19 testing to ambulatory surgery, called the Testing to Surgery Interval Mortality (TSIM) of up to 6 months. Secondary outcome included determining all-cause mortality (TSIM) in time intervals of 0–15 days, 16–30 days, 31–45 days, and 46–180 days in COVID-19 positive and negative patients. 44,934 patients (4297 COVID-19 positive, 40,637 COVID-19 negative) were included in our analysis. COVID-19 positive patients undergoing ambulatory surgery had higher risk of all-cause mortality compared to COVID-19 negative patients (OR = 2.51, p < 0.001). The increased risk of mortality in COVID-19 positive patients remained high amongst patients who had surgery 0–45 days from date of COVID-19 testing. In addition, COVID-19 positive patients who underwent colonoscopy (OR = 0.21, p = 0.01) and plastic and orthopedic surgery (OR = 0.27, p = 0.01) had lower mortality than those underwent other surgeries. A COVID-19 positive diagnosis is associated with significantly higher risk of all-cause mortality following ambulatory surgery. This mortality risk is greatest in patients that undergo ambulatory surgery within 45 days of testing positive for COVID-19. Postponing elective ambulatory surgeries in patients that test positive for COVID-19 infection within 45 days of surgery date should be considered, although prospective studies are needed to assess this. • Positive testing for COVID-19 is associated with a higher risk of all-cause mortality following ambulatory surgery. • This mortality risk is greatest in patients that undergo ambulatory surgery within 45 days of testing positive for COVID-19. • Postponing ambulatory surgeries in patients testing positive for COVID-19 within 45 days of surgery date should be considered. [ABSTRACT FROM AUTHOR]