No description available.

This thesis researched the accuracy of measured energy data in comparison to estimated hand calculation data and estimated building energy performance simulation data. In the facility management industry, there is minimal evidence that building energy performance software is being used as a benchmark against measured energy usage within a building. Research was conducted to find examples of measured energy data compared to simulated data. The study examined the accuracy of a simulation software and hand calculations to measured energy data. Data suggests that comparisons may be made between building energy performance simulated data and measured data, though comparisons are solely based on each individual case. Data suggests that heating load simulation data is more accurate for benchmarks than cooling load simulation data. Importing models into Autodesk Green Building Studio (GBS) was not as successful as was expected. When only four of the initial ten building models chosen imported successfully, the remaining twenty-five other building models were imported. Only two of the twenty-five models successfully imported into GBS. The sample size of this research changed from ten to six. The results of this study show that GBS simulated data was close to actual data for the heating loads. For the cooling loads, however, GBS simulated data was consistently low in comparison to the actual data. The results of this study show that hand calculations were consistently low and not as close as GBS simulated data when compared to the actual data for the heating loads. The opposite was true with the cooling loads as hand calculations were consistently high in comparison to actual data.

No description available.

The global prominence of energy-efficient retrofit in the context of aging properties has garnered noteworthy attention. This surge in interest can be attributed to several advantages, encompassing economically viable carbon dioxide (CO₂) emissions reduction, diminished energy expenditures, and improved indoor air quality. Passive retrofits, such as thermal insulation and fenestration improvement, and active retrofits, such as heating setpoint temperature optimization, offer great potential for CO₂ reduction and energy savings. The central objective of this study is ascertaining the feasibility of attaining a 40% reduction in CO₂ emissions with the lowest cost and with constraints on heating setpoints temperature by finding optimal design parameters encompassing thermal insulation (including both single and double-layer), fenestration, and heating setpoint temperatures. This inquiry is substantiated through a case study of the Leblanc residence on the University of Ottawa campus. In pursuit of this objective, a thermal model of the Leblanc building was developed via EnergyPlus and subsequently subjected to a validation process following ASHRAE Guideline 14. After validation, an array of discrete optimization scenarios was executed using the NSGA-II model, facilitated by the JEPLUS+EA software. This approach aimed to identify the most suitable parameters for achieving optimal CO₂ reduction and cost outcomes. Notably, the results showcased 20 solutions, each boasting a reduction of 40% or more in CO₂ emissions and heating setpoint temperature higher than 18 °C. While the choice to prioritize either cost or CO₂ reduction remains at the user's discretion, four solutions have been discerned as the most effective. Furthermore, the findings suggest that implementing these optimal solutions can significantly decrease CO₂ emissions, ranging between 41.79% and 46.36%. The associated costs were also determined to fall within $36,262 to $57,934.

No description available.

No description available.

No description available.

With the advent of exascale architectures maximizing performance while maintaining energy consumption within reasonable limits has become one of the most critical design constraints. This constraint is particularly significant in light of the power budget of 20 MWatts set by the U.S. Department of Energy for exascale supercomputing facilities. Therefore, understanding an application's characteristics, execution pattern, energy footprint, and the interactions of such aspects is critical to improving the application's performance as well as its utilization of the underlying resources. With conventional methods of analyzing performance and energy consumption trends scientists are forced to limit themselves to a manageable number of design parameters. While these modeling techniques have catered to the needs of current high-performance computing systems, the complexity and scale of exascale systems demands that large-scale design-space-exploration techniques are developed to enable comprehensive analysis and evaluations. In this dissertation we present research on performance and energy modeling of current high performance computing and future exascale systems. Our thesis is focused on the design space exploration of current and future architectures, in terms of their reconfigurability, application's sensitivity to hardware characteristics (e.g., system clock, memory bandwidth), application's execution patterns, application's communication behavior, and utilization of resources. Our research is aimed at understanding the methods by which we may maximize performance of exascale systems, minimize energy consumption, and understand the trade offs between the two. We use analytical, statistical, and machine-learning approaches to develop accurate, portable and scalable performance and energy models. We develop application and machine abstractions using Aspen (a domain specific language) to implement and evaluate our modeling techniques. As part of our research we develop and evaluate system-level performance and energy-consumption models that form part of an automated modeling framework, which analyzes application signatures to evaluate sensitivity of reconfigurable hardware components for candidate exascale proxy applications. We also develop statistical and machine-learning based models of the application's execution patterns on heterogeneous platforms. We also propose a communication and computation modeling and mapping framework for exascale proxy architectures and evaluate the framework for an exascale proxy application. These models serve as external and internal extensions to Aspen, which enable proxy exascale architecture implementations and thus facilitate design space exploration of exascale systems. / Ph. D.

Cette thèse concerne les drones dits "convertibles", qui allient capacité au vol stationnaire et efficacité énergétique en vol de croisière. Les principales contributions de ce travail comportent trois volets. D'abord, nous concevons une nouvelle structure de drone en ajoutant de chaque côté d'un quadrirotor une aile qui peut pivoter autour d'un axe appartenant au plan des hélices. Notre prototype a de nombreux avantages par rapport aux structures convertibles existantes: conception mécanique simple car dérivée d'un quadrirotor classique, flexibilité pour le montage de différents composants (ailes, hélices), etc. Deuxièmement, nous proposons une modélisation énergétique de ce type de drone convertible, en tenant compte de ses caractéristiques par rapport aux hélicoptères avec pilote à bord (grande variation des forces aérodynamiques, dégradation des performances à faible nombre de Reynolds, etc.). Finalement, concernant la conception de la commande, les degrés de liberté des ailes permettent le découplage entre les orientations des hélices et celle des ailes. Cela augmente considérablement les possibilités de contrôle par rapport aux aéronefs traditionnels. S'appuyant sur cette caractéristique, plusieurs approches de contrôle sont proposées. En particulier, en utilisant une conception géométrique spécifique, nous montrons qu'un contrôle efficace peut être obtenu sans mesures de la vitesse air. Les résultats de simulation confortent cette stratégie de contrôle, même en présence de vent fort et variable. Afin de valider la théorie, un prototype mécanique du drone a été construit dans notre laboratoire et des essais en vol préliminaires ont été effectués. / There is a growing interest to design convertible aerial vehicles that can hover like helicopters and fly forward efficiently like airplanes. This thesis is devoted to the conception, modeling, and control of such a convertible mini-UAV (Unmanned Aerial Vehicle). The main contributions of this work are threefold. Firstly, we design a novel UAV structure by adding to each side of a quadrotor one wing that can rotate around an axis belonging to the propellers' plane. Our prototype has many advantages over existing convertible structures: simple mechanical concept since inspired by a classical quadrotor, flexibility for selecting different components (wings, propellers), flexibility for the control design, etc. Secondly, we provide an energy modeling of this type of convertible UAVs, taking into account their characteristics as compared to full-scale helicopters (large variation of aerodynamic forces, performance degradation at low Reynolds number, etc.). Finally, as for the control design, the degrees of freedom of the wings permit the decoupling between propellers and wings' orientations. This greatly enhances the control flexibility as compared to traditional aircraft. Relying on this feature, several control approaches are proposed. In particular, using a specific geometrical design, we show that an efficient control of our UAV can be obtained without air-velocity measurements. Simulation results confirm the soundness of our control design even in the presence of strong and varying wind. En route to validate the theory, a mechanical prototype of the UAV was constructed in our laboratory and preliminary flight tests were performed.

abstract: City administrators and real-estate developers have been setting up rather aggressive energy efficiency targets. This, in turn, has led the building science research groups across the globe to focus on urban scale building performance studies and level of abstraction associated with the simulations of the same. The increasing maturity of the stakeholders towards energy efficiency and creating comfortable working environment has led researchers to develop methodologies and tools for addressing the policy driven interventions whether it’s urban level energy systems, buildings’ operational optimization or retrofit guidelines. Typically, these large-scale simulations are carried out by grouping buildings based on their design similarities i.e. standardization of the buildings. Such an approach does not necessarily lead to potential working inputs which can make decision-making effective. To address this, a novel approach is proposed in the present study. The principle objective of this study is to propose, to define and evaluate the methodology to utilize machine learning algorithms in defining representative building archetypes for the Stock-level Building Energy Modeling (SBEM) which are based on operational parameter database. The study uses “Phoenix- climate” based CBECS-2012 survey microdata for analysis and validation. Using the database, parameter correlations are studied to understand the relation between input parameters and the energy performance. Contrary to precedence, the study establishes that the energy performance is better explained by the non-linear models. The non-linear behavior is explained by advanced learning algorithms. Based on these algorithms, the buildings at study are grouped into meaningful clusters. The cluster “mediod” (statistically the centroid, meaning building that can be represented as the centroid of the cluster) are established statistically to identify the level of abstraction that is acceptable for the whole building energy simulations and post that the retrofit decision-making. Further, the methodology is validated by conducting Monte-Carlo simulations on 13 key input simulation parameters. The sensitivity analysis of these 13 parameters is utilized to identify the optimum retrofits. From the sample analysis, the envelope parameters are found to be more sensitive towards the EUI of the building and thus retrofit packages should also be directed to maximize the energy usage reduction. / Dissertation/Thesis / Masters Thesis Architecture 2017