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The effective operation of HVAC systems is crucial to minimize energy inefficiencies and occupant discomfort. However, these systems can experience various problems, including hardware and software-related anomalies. In contrast to most existing fault detection and diagnostic approaches, which rely on simple rules and alarms, this study introduces novel unsupervised approaches for detecting zone anomalies in variable air volume (VAV) air handling units (AHUs). The methods utilize autoencoders (AE) and principal component analysis (PCA). To evaluate the effectiveness of the proposed methods, both a synthetic dataset and measured data from a 28-zone VAV AHU system were investigated. The proposed method successfully detected several zone temperature and airflow anomalies using the AE-based method, and several zone anomalies were also identified using the PCA-AE approach by considering four commonly available zone-level trend logs in VAV AHUs namely temperature, airflow, airflow set-point, and VAV terminal damper position. The findings demonstrated the great adaptability of the proposed methods in detecting a wide range of zone anomalies in any modern building equipped with VAV AHUs, giving operators valuable insights about the system and notifying them of potential faults at an early stage. [ABSTRACT FROM AUTHOR]

Objective : Subarachnoid hemorrhage (SAH) is a condition characterized by bleeding in the subarachnoid space, often resulting from the rupture of a cerebral aneurysm. Delayed cerebral ischemia caused by vasospasm is a significant cause of mortality and morbidity in SAH patients, and inflammatory markers such as systemic inflammatory response index (SIRI), systemic inflammatory index (SII), neutrophil-to-lymphocyte ratio (NLR), and derived NLR (dNLR) have shown potential in predicting clinical vasospasm and outcomes in SAH patients. This article aims to investigate the relationship between inflammatory markers and cerebral vasospasm after aneurysmatic SAH (aSAH) and evaluate the predictive value of various indices, including SIRI, SII, NLR, and dNLR, in predicting clinical vasospasm. Methods : A retrospective analysis was performed on a cohort of 96 patients who met the inclusion criteria out of a total of 139 patients admitted Trakya University Hospital with a confirmed diagnosis of aSAH between January 2013 and December 2021. Diagnostic procedures, neurological examinations, and laboratory tests were performed to assess the patients' condition. The Student's t-test compared age variables, while the chi-square test compared categorical variables between the non-vasospasm (NVS) and vasospasm (VS) groups. Receiver operating characteristic (ROC) curve analyses were used to evaluate the diagnostic accuracy of laboratory parameters, calculating the area under the ROC curve, cut-off values, sensitivity, and specificity. A significance level of p<0.05 was considered statistically significant. Results : The study included 96 patients divided into two groups : NVS and VS. Various laboratory parameters, such as NLR, SII, and dNLR, were measured daily for 15 days, and statistically significant differences were found in NLR on 7 days, with specific cut-off values identified for each day. SII showed a significant difference on day 9, while dNLR had significant differences on days 2, 4, and 9. Graphs depicting the values of these markers for each day are provided. Conclusion : Neuroinflammatory biomarkers, when used alongside radiology and scoring scales, can aid in predicting prognosis, determining severity and treatment decisions for aSAH, and further studies with larger patient groups are needed to gain more insights. [ABSTRACT FROM AUTHOR]

Initial and maintenance costs often prevent dense submeter installations that enable room-level thermal energy monitoring. Previous studies suggested that building automation system (BAS) trend data represents an untapped potential to disaggregate existing meter data for heating and cooling into device- and system-level end-uses. These techniques disaggregate meter data by analyzing trend data that provide contextual information regarding the operating status of energy-consuming equipment. However, the level of submetering required to enable end-use disaggregation has yet to be studied. To this end, this paper investigates the effect of submeter density and configuration on the performance of a regression-based disaggregation strategy using BAS trend data as predictors. The method was evaluated in two steps; first, using synthetic meter and BAS trend data generated by a building performance simulation (BPS) model of a government office building, and second, with submeter data from a real office building. The results highlight the factors affecting the minimum number of heating energy submeters needed to be installed in both buildings for accurate device- and system-level disaggregation. The methodology presented in the paper can also inform changes in building design codes and standards regarding the minimum density and appropriate configuration for submetering. [ABSTRACT FROM AUTHOR]

This paper classifies common sequencing logic faults in multiple zone variable air volume (VAV) air handling unit (AHU) systems by conducting a critical review of the literature. Six broad categories of sequencing logic faults are identified affecting the state of operation, mode of operation, and supply air temperature and duct static pressure setpoint reset programs. A case study with building automation system (BAS) trend data from two VAV AHU systems is conducted to provide examples of these sequencing logic faults. Four of the six fault types are found present in at least one of the two AHUs. The most significant fault discovered was an incorrect reference to a nonexistent VAV terminal damper address forcing an unnecessarily high duct static pressure setpoint and wasting fan electricity. The BAS programs are reviewed in consultation with the operations staff, and the coding mistakes leading to the detected anomalous behavior are identified. [ABSTRACT FROM AUTHOR]

Building performance optimization is a powerful technique that assists designers in identifying optimal design solutions. However, occupant-related uncertainty is considered a major challenge that can influence the credibility of the selected optimal design. This paper proposes an occupant-centric method for window and shading design optimization in office buildings that assists designers in evaluating and handling the impact of occupant-related assumptions. The method consists of three major steps: generating occupant scenarios, conducting occupant-centric mathematical optimization, and deriving rules for selecting optimal design parameters using decision trees. An office building model with numerous possible window-related parameters was used to demonstrate the proposed optimization process. Sixty-four different occupant scenarios were considered. The results of the optimization process demonstrated that occupant-related assumptions are crucial in determining optimal window and shading design, as different occupant scenarios led to different optimal solutions. The study findings also provided insights to building designers regarding classifying and selecting window and shading design parameters under varying occupant scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a systematic method to select an inverse blackbox model that can characterize the building-level heating and cooling load patterns parsimoniously. To this end, hourly heating, cooling, and electrical load data were gathered from five office buildings. In addition, concurrent weather data for temperature, solar irradiance, wind speed, and humidity were collected. Using the recent history of weather and electrical load data from the past three hours, 18 different forms of model at varying number of inputs and parameters were formulated for each of the five buildings. After assessing the models’ performance through a cross-validation and a residual analysis, one of the models was selected. The selected model was the one with a one-layer artificial neural network, six inputs, and a one-hour input history. Then, through illustrative examples, different use-cases in which inverse blackbox models can support the operational decision making process are discussed. [ABSTRACT FROM AUTHOR]

Fault impact analysis is an important phase of fault detection and diagnosis (FDD), enabling prioritization of the faults detected and allocation of resources to procure services and materials. However, many existing fault impact analysis workflows rely on building performance simulation (BPS) models with high engineering cost, limiting their use in FDD software. To this end, this paper introduces an inverse greybox model-based fault impact analysis method using building automation system (BAS) trend data from variable air volume (VAV) air handling unit (AHU) systems to quantify the heating and cooling energy impact of control system faults. It trains inverse models of a VAV AHU system by employing a sequential parameter estimation algorithm, formulates virtual meters quantifying the sensible heat transfer rate from AHU heating and cooling coils and the perimeter heating devices, and employs the virtual meters to estimate heating and cooling energy use of an existing VAV AHU system and the same system after fault correction. The method was used with a synthetic BAS dataset generated by using an EnergyPlus model of a 26-zone VAV AHU system in Ottawa, Canada. The results indicate that the virtual meters could estimate the monthly energy use for heating and cooling at a coefficient of variation of the root mean squared error (CV(RMSE)) of 4–6%. The method was demonstrated with measured BAS data from a 72-zone VAV AHU system, revealing an energy savings potential of 43% for heating and 22% for cooling through fault correction. [ABSTRACT FROM AUTHOR]

This paper explores how effectively building variable air volume air handling unit (VAV AHU) configurations supply required outdoor air to building spaces using data analysis and building performance simulation. One year's worth of zone-level occupancy data from a floor of an institutional office building was estimated using Wi-Fi device count and concurrent motion detector and CO2 sensor data. Zone-level ventilation rates were compared to these data to calculate per person ventilation rates. The results indicated that some spaces experienced under-ventilation for up to 34% of occupied hours. For the simulation-based investigation, a 27-zone energy model was used. Occupancy data from the building were used in the simulation. Four operation modes were simulated: default operation, occupancy-based demand-controlled ventilation (DCV), occupancy-based VAV control, and a combination of the latter strategies. The fewest instances of under-ventilation occurred with occupancy-based VAV control. The under-ventilation instances were a result of inefficient distribution of outdoor air across building zones. The supply of standard (10 L/s per person) ventilation with occupancy-based VAV control, instead of a default constant minimum ventilation rate, reduced the number of under-ventilation instances by ∼80% while reducing the energy use by ∼30%. [ABSTRACT FROM AUTHOR]

Following our previous study, which indicated that several occupant-related assumptions in the National Building Code (NBC) of Canada are different from findings in recent measurement-based studies, this study aims to: i) quantify the direct energy impact associated with discrepancies between the current code's occupant-related assumptions and those obtained from recent measurement-based studies and ii) demonstrate how key NBC requirements could be reevaluated as a result of the new/proposed occupant-related assumptions. In this regard, this paper applies energy modeling and life cycle costing (LCC) to 11 representative archetypes across different Canadian climate zones. First, EnergyPlus simulations were conducted to evaluate the energy impact of the proposed and existing occupant-related assumptions. Second, LCC was used to evaluate code requirements' economic implications under these two sets of occupant assumptions. Our results indicate that the default occupant-related assumptions used by NBC generally lead to higher predicted heating, but lower cooling, energy consumption. However, heating energy is more significant since heating energy use is typically an order of magnitude higher than cooling energy use for Canadian homes. Our analysis also indicates that the current occupant-related NBC assumptions yield different optimal potential code-compliant upgrades in some cases relative to the new energy-related occupant assumptions. [ABSTRACT FROM AUTHOR]

A primary strategy for the energy-efficient operation of commercial office buildings is to deliver building services, including lighting, heating, ventilating, and air conditioning (HVAC), only when and where they are needed, in the amount that they are needed. Since such building services are usually delivered to provide occupants with satisfactory indoor conditions, it is important to accurately determine the occupancy of building spaces in real time as an input to optimal control. This paper first discusses the concepts of building occupancy resolution and accuracy and briefly reviews conventional (explicit) occupancy detection approaches. The focus of this paper is to review and classify emerging, potentially low-cost approaches to leveraging existing data streams that may be related to occupancy, usually referred to as implicit/ambient/soft sensing approaches. Based on a review and a comparison of related projects/systems (in terms of occupancy sensing type, resolution, accuracy, ground truth data collection method, demonstration scale, data fusion and control strategies) the paper presents the state-of-the-art of leveraging existing occupancy-related data for optimal control of commercial office buildings. It also briefly discusses technology trends, challenges, and future research directions. [ABSTRACT FROM AUTHOR]

• Three inverse modelling approaches are used to create radiant heater virtual meters. • Zone models using steady-state and transient greybox approaches are developed. • Zone models using water side load disaggregation approach are developed. • Virtual meters accuracy is used to evaluate the performance of the models. • Modelling approaches' data requirements, data processing, and accuracy are compared. Virtual metering at the equipment level provides a non-intrusive tool to understand energy flows in commercial buildings and monitor building energy performance. The accuracy of virtual meters depends on the modelling approach and its ability to capture the underlying physical processes. This paper presents a novel comparison and provides guidance for the selection of inverse modelling approaches used to create virtual meters to estimate the heat supplied by zone-level hydronic perimeter heaters. The approaches investigated are: steady-state inverse greybox modelling, transient inverse greybox modelling, and water-side load disaggregation. Models that represent these three approaches are trained using data from a highly instrumented academic building in Ottawa, Canada. Model parameters are identified using the genetic algorithm and used in creating virtual meters that can estimate the heat added by perimeter heaters into building variable air volume zones. The accuracy of these virtual meters is assessed by comparing them to physical meters installed in these zones. The results indicate that the three modelling approaches can estimate the daily heating energy supplied by perimeter heaters at a normalized root-mean-square error between 16% and 18%. However, the accuracy of the virtual meters declines as the number of zones increases. The performance of the three modelling approaches is evaluated through illustrative examples that compare modelling assumptions, data requirements, data processing, and model prediction accuracy. [ABSTRACT FROM AUTHOR]

• A survey was administered in QC, Canada to investigate participation in DR Programs. • Results characterized contextual factors that influence participation in DR. • Results identified significant potential to expand DR programs in the province. • Respondents believed "smart" home appliances can improve their participation in DR. • Results provided valuable insights to further develop DR programs in North America. The current push towards electrification in various sectors as a decarbonization strategy presents significant challenges for utility providers due to unintended consequences related to higher peak demand. To ensure grid reliability, utilities resort to importing costly electricity or using fossil fuels, which counteracts these decarbonization efforts. To address this issue, they introduced demand-side management strategies, known as demand-response (DR) programs, to engage customers in reducing peak demand. While commercial and industrial customers have greater load flexibility, the significance of residential buildings in managing peak demand is growing due to electrification of heating systems and the proliferation of electric vehicles (EV). However, the development of residential DR programs lags behind their potential, facing challenges such as limited customer knowledge, price elasticity, and infrastructure costs. To increase residential DR program adoption, understanding occupants' perceptions and behaviors is vital. This paper presents the results of a survey conducted in Quebec to identify participation rates, incentives, approaches, and contextual factors influencing enrolment in DR programs and participation in peak demand events. It was found that there is significant potential for expanding these programs, especially by identifying key demographic sectors that may be more likely to participate compared to the current participant breakdown. The research also uncovered contextual factors and demographic variables that statistically influenced occupant behavior during peak demand events and their impact on energy-saving actions. Notably, more than 80 % of respondents believed that having smart thermostats would facilitate their participation in DR programs. These results offer valuable insights for further development and enhancement of DR programs in the province. [ABSTRACT FROM AUTHOR]

• The impact of occupants' distributions on building performance is explored. • Two new comfort performance metrics are introduced. • Benefits of DCV for handling variable occupants' distributions are explored. • Recommendations are provided for handling uncertainty from occupants' distribution. Designers typically follow a simplistic approach regarding occupant distribution in building simulation models, such as a uniform distribution. However, this approach rarely reflects reality, as occupants are often distributed heterogeneously in building spaces. To this end, this study develops a methodology to examine the impact of occupant distribution on energy and comfort performance; it then applies the methodology to an office building model in Toronto, Canada. The building energy model was simulated under a set of seventy-five plausible occupants' distribution scenarios (ODSs). The results indicate that ODSs can have a significant impact on occupants' comfort as densely populated zones experienced significantly higher discomfort hours per occupant compared to the homogeneous distribution. On the other hand, the variable ODSs had a modest impact on energy performance as the highest difference in energy use intensity was observed to be about 9%, given that the model heating, ventilation, and air conditioning was hard-sized for all simulations. Finally, the benefits of deploying adaptive technologies such as demand-controlled ventilation were assessed in terms of mitigating the impact of variable ODSs. [ABSTRACT FROM AUTHOR]

• A repository on occupant-centric control and operation case studies is proposed. • A survey was developed to collect and present the data systematically. • The current database includes 58 OCC case studies with different approaches. • Results show a lack of studies on lighting and building operators. • Occupant modeling is more commonly implemented for responsive controls. Occupant-centric controls (OCC) and operations have emerged as a key concept in shifting the focus from conventional building- (or system-) centric operations to a more occupant-centric approach. Despite the potential of OCC to meet occupants' demands and bridge buildings' energy performance gap, its implementation in real-world settings has been limited. In addition, there is a lack of standardization in methodologies and terms to facilitate meaningful comparisons among case studies. Therefore, this paper aims to present a repository of OCC case studies, offering a platform for standardization and presenting key information about practical implementations of these strategies in real-world scenarios. To accomplish this, descriptors, terms, and concepts about OCC case studies were discerned through a literature review. These elements were systematically integrated into a structured survey to capture comprehensive information on OCC field study implementations. This paper provides an overview of the survey structure and insights into the current dataset, which comprises a total of 58 OCC case studies. By publishing the case study repository, we intend to establish standard categories for OCC strategies and to offer researchers and practitioners a database through which they can understand trends and possibilities for implementing OCC strategies. [ABSTRACT FROM AUTHOR]