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High performance of HVAC connected Building Integrated Photovoltaic/Thermal (BIPV/T) systems relies on appropriate control. However, optimal control is often overlooked, resulting in systems that operate inefficiently. This paper investigates how model predictive control (MPC) can improve the operation of open loop air-based BIPV/T systems connected to multiple thermal applications. The BIPV/T system at the first institutional net-zero energy building in Canada, the Varennes library, is used as an archetype. The BIPV/T covers 16% of the south-facing roof and operates under a simple rule-based control strategy. The developed control and design strategies consider variations of this system, to achieve higher thermal utilization efficiency. A control-oriented BIPV/T model is developed and calibrated using monitored data. The BIPV/T airflow is regulated through MPC for simultaneous heat supply to an Energy Recovery Ventilator and air-to-water heat pump. The BIPV/T air flow is efficiently controlled, considering the connected thermal applications, environmental conditions, and PV temperature. Model-based control for BIPV/T systems can increase the amount of useful heat and reduce PV overheating. The MPC controller for the examined system reduced the building energy consumption compared to the business-as-usual operation by 40% and together with increased BIPV/T area can supply excess heat to adjacent buildings. [ABSTRACT FROM AUTHOR]

The enactment of COVID-19 policies in Canada falls under provincial jurisdiction. This study exploits time-series variation across four Canadian provinces to evaluate the effects of stricter COVID-19 policies on daily case counts. Employing data from this time-period allows an evaluation of the efficacy of policies independent of vaccine impacts. While both OLS and IV results offer evidence that more stringent Non-Pharmaceutical Interventions (NPIs) can reduce daily case counts within a short time-period, IV estimates are larger in magnitude. Hence, studies that fail to control for simultaneity bias might produce confounded estimates of the efficacy of NPIs. However, IV estimates should be treated as correlations given the possibility of other unobserved determinants of COVID-19 spread and mismeasurement of daily cases. With respect to specific policies, mandatory mask usage in indoor spaces and restrictions on business operations are significantly associated with lower daily cases. We also test the efficacy of different forecasting models. Our results suggest that Gradient Boosted Regression Trees (GBRT) and Seasonal Autoregressive-Integrated Moving Average (SARIMA) models produce more accurate short-run forecasts relative to Vector Auto Regressive (VAR), and Susceptible–Infected–Removed (SIR) epidemiology models. Forecasts from SIR models are also inferior to results from basic OLS regressions. However, predictions from models that are unable to correct for endogeneity bias should be treated with caution. [ABSTRACT FROM AUTHOR]

Calves born on Eastern Canadian dairy farms that are not kept in the herds are traditionally sold through auction markets and are raised for meat purposes such as veal calves. Since February 2020, a new Canadian federal regulation has forbidden calves <9 d old to be sold through auction markets. However, in the absence of a real-time birth registry consultation system, it would be of interest to look for predictors that could be associated with age to allow identification of calves too young to be transported. In the current retrospective cross-sectional study, 1,178 calves with a declared birth date (411 calves aged <9 d old; 34.9%) were assessed in 2 large Québec auction sites. Easy-to-record covariates [body weight (BW), breed phenotype, and presence of an umbilical cord remnant] as well as other clinical signs (umbilical swelling, enlargement, umbilical pain, wet umbilicus, skin tent, sunken eyes, ocular and nasal secretion, and hide cleanliness) were assessed. Two logistic regression models using age as a dichotomous dependent variable (<9 d old vs ≥9 d old) were built. The first model (model 1) considered all covariates, which were selected after univariable analyses and a backward stepwise selection process, whereas a more pragmatic model (model 2) only included the 3 easy-to-record variables (i.e., BW, breed, umbilical cord). Both models had similar accuracy to detect calves <9 d old (sensitivity of 38.4 and 37.5%, and specificity of 85.7 and 84.6% for model 1 and 2, respectively). Model 2 was subsequently more specifically studied as it employs a faster and easier assessment. Decision thresholds were tested for their robustness based on misclassification cost term (MCT) analysis with various prevalence of calves <9 d old and various costs of false-negative:false-positive ratio. Despite statistical significance, model accuracy, even if refined with MCT analysis, was limited at the individual level, showing the limits of using physical signs and BW or their combination as a reliable proxy of age. The sensitivity of these models to find calves <9 d old was not to be used for monitoring compliance with the Canadian federal regulation. The relatively high model specificity may help to use this model as a rule-in test (i.e., targeting positive calves for further investigation) rather than a rule-out test (due to its low sensitivity). [ABSTRACT FROM AUTHOR]

In high-performance sport, an athlete's ability to overcome setbacks and sustain their pursuit of long-term goals is essential for success. Grit (i.e., passion and perseverance over long-terms) has been linked to success in a variety of domains but is often critiqued for its limited predictive utility when compared to other psychological variables including self-control, conscientiousness, and mental toughness. The purpose of this study was to examine whether grit predicted important athlete outcomes (i.e., various measures of sport performance and athlete well-being) beyond other determinants of success. Data from 214 collegiate student-athletes (111 women, 103 men; M age = 21.02, SD = 2.26) from Western Canadian universities were analysed. When predicting performance, the addition of the grit subscales (i.e., consistency of interests, perseverance of effort, adaptability to situations) explained an additional 11% of variance (R 2 = 0.37, F [7, 203] = 7.16, p <.001) beyond self-control, conscientiousness, and mental toughness in subjective sport performance perceptions; however, grit did not add unique variance when entered into models predicting athlete goal achievement perceptions or highest level of competition. When predicting well-being, addition of the grit subscales added 18% of unique variance (R 2 = 0.43, F [7, 203] = 21.43, p <.001) beyond other determinants of success in eudaimonic well-being, and 5% (R 2 = 0.17, F [7, 203] = 6.95, p <.001) in satisfaction with sport, but did not add any unique variance to the model predicting mood. The partial support of the predictive utility of grit illustrates the complexity of forecasting success in sport and offers evidence that grit should continue to be studied as a motivational disposition in the domain of sport. • Grit subscales (PE, CI, AS) added unique variance to models predicting performance and well-being. • PE was a significant predictor of some measures of performance and well-being, while CI was not. • A new grit subscale—AS—was a significant predictor of some measures of performance and well-being. • Mental toughness was the most consistent predictor of our six performance and well-being. • Conscientiousness and self-control did not significantly contribute to regression models. [ABSTRACT FROM AUTHOR]

This article presents the results of a 182 day implementation of a model predictive controller (MPC) in a test house in Ottawa, Canada. The MPC controls the timing of hydronic floor heating and cooling within the highly glazed house (13.5% south-facing window-to-floor ratio). The objectives of the MPC are to optimally control the on/off times of active heating and cooling to minimize energy use while also minimizing air temperature violations outside of a prescribed range. It was found that 71% of the days in the experimental period saw successful operation of the MPC towards these goals. Days that experienced overheating were found to be concentrated in January–February and days that experienced overcooling were found to be concentrated in March–May. Problematic decision making by the MPC was found to be rooted in 4 main issues: (1) global horizontal irradiance (GHI) is a bad indicator of passive solar on its own; (2) weather forecasting (specifically GHI forecasting) is critical to MPC success in highly glazed buildings; (3) the GHI history regressor used in the prediction model regularly influenced predictions illogically; and (4) the MPC lacked decision-making logic on the timing of hydronic floor cooling. • A model predictive controller was implemented in a highly glazed, full-scale, test house. • Performance of the model predictive controller was evaluated over 7 months. • The design of the MPC is simple and practical for implementation in a real building. • A recommendation is made on what a model predictive controller could look like for a highly glazed house. [ABSTRACT FROM AUTHOR]

As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential. Methods for status monitoring with consideration to the weather condition and forecasting with the use of in-service data from ships requires accurate and complete models for predicting the energy efficiency of a ship. The models need to effectively process all the operational data in real-time. This paper presents models that can predict fuel consumption using in-service data collected from a passenger ship. Statistical and domain-knowledge methods were used to select the proper input variables for the models. These methods prevent over-fitting, missing data, and multicollinearity while providing practical applicability. Prediction models that were investigated include multiple linear regression (MLR), decision tree approach (DT), an artificial neural network (ANN), and ensemble methods. The best predictive performance was from a model developed using the XGboost technique which is a boosting ensemble approach. Our code is available on GitHub at https://github.com/pagand/model%5foptimze%5fvessel/tree/OE for future research. • Comparison of fuel consumption models for west coast Canada vessel • Feature engineering utilized domain knowledge to identify key variables • Parametric and non-parametric models used for prediction model development • Evaluation included running time, mean error, and future step prediction error. [ABSTRACT FROM AUTHOR]

Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R2 = 0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R2 = 0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints. [Display omitted] • Eleven machine learning techniques were compared for predicting emerging DBPs. • Support vector regression and Gaussian process regression are best-performing. • Support vector regression reduced 90 % error compared to linear regression. • Gaussian process regression reduced 67 % error compared to linear regression. • The two advanced regression techniques also showed the lowest prediction variance. [ABSTRACT FROM AUTHOR]

Seismic site characteristics, specifically depth to stiff glaciated Pleistocene sediments (z gl) and shear wave velocity (Vs) profiles, of the Fraser River Delta (FRD), located in southern Metropolitan (Metro) Vancouver, British Columbia, Canada are determined using the most comprehensive geodatabase to date. The relationships between z gl and theoretical fundamental frequency determined from known thickness of post-glacial sediments and experimental peak frequency from microtremor horizontal-to-vertical ratios (f 1,HVSR) are investigated. A z gl predictive model based on f 1,HVSR is developed for shallow FRD sites (z gl ≤ 56 m) and is compared to existing microtremor horizontal-to-vertical spectral ratio (MHVSR) peak frequency-sediment thickness models developed in other regions in the world. At 16 FRD sites, a combination of active- and passive-source surface wave array methods are performed to obtain the fundamental-mode Rayleigh wave dispersion curve which is jointly inverted with the MHVSR fundamental peak frequency (f 0,HVSR) using three parametrization models with varied layering. The jointly inverted Vs profiles with their uncertainties are presented for the 16 sites and compared with existing proximal Vs profiles measured by other in situ Vs profiling methods. The comparison shows a notable match (9.1% depth-averaged absolute relative difference) to a significant depth of 220 m from the two datasets. In addition, 8 of the developed Vs profiles are co-located with strong-motion accelerograph stations, which allows for correlation between recorded earthquake ground motions and seismic site parameters. The proposed predictive z gl model and validation of evolving non-invasive in situ Vs profiling methods are important for 1D and 2D seismic site effects quantification and mapping in the FRD. • Vs profiles in the Fraser River Delta are established employing non-invasive surface wave methods. • A predictive model to estimate the thickness of post-glacial sediments based on f 1,HVSR is developed. • 16 Vs profiles are obtained from joint inversion of the fundamental-mode Rayleigh wave dispersion curve with MHVSR f 0,HVSR. • The inverted Vs profile matched nearby existing invasive Vs profiles up to 220 m depth. [ABSTRACT FROM AUTHOR]

• A novel hybridization scheme using both stochastic and deterministic techniques. • Achieves an optimum operational trajectory under forecast uncertainties. • Simplified and computationally efficient problem formulation. • Reduced dependence on frequently updated forecasts. • Proof-of-concept is depicted on a remote off-grid PV-Diesel-Battery power system. With the precipitated integration of intermittent renewable sources, operation optimization employed with uncertainty management strategies is imperative to ensure a reliable, cost-effective, and secure energy supply for the remote off-grid power systems. This paper presents a two-stage operation optimization process embedded in a model predictive control framework. The first stage decision variables are derived in a deterministic optimization framework to achieve minimum operational costs and emissions. The second stage uses a stochastic optimization framework to refine the first stage decision variables to achieve a feasible operation considering several candidate scenarios in a computationally inexpensive manner. Several measures are integrated to the proposed framework in order to address the operational requirements pertaining to remote off-grid power systems. The effectiveness of the proposed framework is demonstrated through numerical experiments for an isolated remote power system in Northern Canada for both summer and winter seasons. Quality of the obtained results as well as the computation efficiency of the overall framework has been verified compared to the existing energy management techniques. The overall result confirms the applicability of the proposed method in achieving a cost-effective and environmentally friendly operational trajectory while effectively accounting for the underlying uncertainties with a reduced computational burden. [ABSTRACT FROM AUTHOR]