This thesis presents three emerging computational methods: machine learning, gradient-free optimization, and Bayesian modelling. Each method is showcased in its ability to enable energy savings in new and existing buildings when paired with dynamic energy models. Machine learning algorithms provide rapid computational speed increases when used as surrogate models, supporting early-stage designs of buildings. Genetic algorithms support the design of complex interacting systems in a reduced amount of effort. Finally, Bayesian modelling can be leveraged to incorporate uncertainty in building energy model calibration. These methods are all readily available and user-friendly, and can be incorporated into current engineering workflows. / Graduate

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A modeling tool was developed capable of evaluating the thermal performance of a commercial building, for the purpose of objectively quantifying the impacts of both operational changes and technological retrofits. The modeling tool was created using a steady state energy balance approach, discretized into half hour time steps to capture the time varying characteristics of the rate of heat transfer through the building envelope, the ventilation systems, appliance heat gains, heat generated by electricity consumption, solar energy transfer and space heating through exhaust gas energy recovery with the TEG POWER system. Several experimental facilities were used to validate the modeling tool, and to provide inputs to the case studies presented. Data from two separate commercial baking operations was collected, and was shown to be in agreement with the model predictions with a 7% error. Several energy conservation measures were simulated, including switching to idealized methods of exhaust ventilation, sealing and insulating appliances, shutting down appliances during unoccupied hours, and the inclusion of exhaust gas energy harvesting. Implementing all four conservation measures at a single restaurant had the effect of reducing electricity consumption by 14% or approximately 17,700 kWh (64 GJ), and reducing natural gas consumption by 60% or approximately 18,200 m3 (608 GJ) annually. In contrast, proceeding directly to the energy harvesting solution, and bypassing other conservation measures, only allowed for 20% of the total potential energy savings to be realized. If the concepts identified are implemented across 2000 comparable restaurants in Ontario, there is a potential to reduced electricity consumption by 44.4 million kWh and natural gas consumption by 33.7 million cubic meters annually. The measures would effectively eliminate 65,500 metric tonnes of CO2 emissions every year. / Thesis / Master of Applied Science (MASc)

This study sought to demonstrate the potential applications of the solar chimney for the naturally ventilating a building. Computational fluid dynamics (CFD) was used to model various room configurations to assess ventilation strategies. A parametric study of the solar chimney system was executed, and three-dimensional simulations were compared and validated with experiments. A new definition for the hydraulic diameter that incorporated the chimney geometry was developed to predict the flow regime in the solar chimney system. To mitigate the cost and effort to use experiments to analyze building energy, a mathematical approach was considered. A relationship between small- and full-scale models was investigated using non-dimensional analysis. Multiple parameters were involved in the mathematical model to predict the air velocity, where the predictions were in good agreement with experimental data as well as the numerical simulations from the present study. The second part of the study considered building design optimization to improve ventilation using air changes per hour (ACH) as a metric, and air circulation patterns within the building. An upper vent was introduced near the ceiling of the chimney system, which induced better air circulation by removing the warm air in the building. The study pursued to model a realistic scenario for the solar chimney system, where it investigated the effect of the vent sizes, insulation, and a reasonable solar chimney size. It was shown that it is critical to insulate the backside of the absorber and that the ratio of the conditioned area to chimney volume should be at least 10. Lastly, the application of the solar chimney system for basement ventilation was discussed. Appropriate vent locations in the basement were determined, where the best ventilation was achieved when the duct inlet was located near the ceiling and the exhaust vent was located near the floor of the chimney. Sufficient ventilation was also achieved even for scenarios of a congested building when modeling the presence of multiple people. / Ph. D.

With growing concerns on global energy demand and climate change, it is important to focus on efficient utilization of electricity in commercial buildings, which contribute significantly to the overall electricity consumption. Accordingly, there has been a number of Building Energy Management (BEM) software/hardware solutions to monitor energy consumption and other measurements of individual building loads. BEM software serves as a platform to implement smart control strategies and stores historical data. Although BEM software provides such lucrative benefits to building operators, in terms of energy savings and personalized control, these benefits are not harnessed by most small to mid-sized buildings due to a high cost of deployment and maintenance. A cloud-based BEM system can offer a low-cost solution to promote ease of use and support a maintenance-free installation. In a typical building, a conventional router has a public address and assigns private addresses to all devices connected to it. This led to a network topology, where the router is the only device in the Internet space with all other devices forming an isolated local area network behind the router. Due to this scenario, a cloud-based BEM software needs to pass through the router to access devices in a local area network. To address this issue, some devices, during operation, make an outbound connection to traverse through the router and provide an interface to itself on the Internet. Hence, based on their capability to traverse through the router, devices in a local area network can be distinguished as cloud and non-cloud devices. Cloud-based BEM software with sufficient authorization can access cloud devices. In order to access devices adhering to non-cloud protocols, cloud-based BEM software requires a device in the local area network which can perform traversal through the router on behalf of all non-cloud devices. Such a device acts as an IoT gateway, to securely interconnect devices in a local area network with cloud-based BEM software. This thesis focuses towards architecting, designing and prototyping an Internet-of-Things (IoT) gateway which can perform traversal on behalf of non-cloud devices. This IoT gateway enables cloud-based BEM software to have a comprehensive access to supported non-cloud devices. The IoT gateway has been designed to support BACnet, Modbus and HTTP RESTful, which are the three widely adopted communication protocols in the building automation and control domain. The developed software executes these three communication protocols concurrently to address requests from cloud-based BEM system. The performance of the designed architecture is independent of the number of devices supported by the IoT gateway software. / Master of Science

Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector. Energy management tools have been developed to promise appropriate energy savings. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios, while building automation systems (BAS) works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. By integrating the energy simulated data and actual operational data, the accuracy of a building energy model can be increased while the calibrated energy model can be applied as a benchmark for guiding the operational strategies. This research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. A systematic literature review has been conducted in investigating the most popular used data formats and data exchange methods for the integration of BIM/BES and BAS, the results showed the industry foundation classes (IFC) was the most common choice for BIM tools mainly and database is a key solution for managing huge actual operational datasets, which was a reference for the next step in research. Then a BIM-based framework was proposed to supporting the data exchange process among BIM/BES/BAS. 4 modules including BIM Module, Operational Data Module, Energy Simulation Module and Analysis and Visualization Module with an interface were designed in the framework to document energy-related information and to allow its exchange between BES and BAS. A prototype of the framework was developed as a platform and a case study of an entire office suite was conducted using the platform to validate this framework. The results showed that the proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity. / Doctor of Philosophy / Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector, promising appropriate energy savings can be achieved over the life cycle of buildings through proper design, construction, and operation. Energy management tools have been developed towards this end. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios. These instances include the selection of both new and retrofit designs and for building codes, building commissioning, and real-time optimal control, among others. The main challenge surrounding BES is the discrepancy between quantitative results and actual performance data. Building automation systems (BAS), or a part of BAS which is often referred to as building energy management systems (BEMS), works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. The key disadvantage to the more general tool of BAS in energy management is that the data sets collected by BAS are typically too large to be analyzed effectively. One potential solution to the lack of effective energy management analysis may lie in the integration of BES and BAS. Actual operational data can be compared with simulation results in assessing the accuracy of an energy model while the energy model can be applied as a benchmark for evaluating the actual energy consumption and optimizing control strategies. The presented research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. More specifically, this research posits the framework of integrating BIM, BES, and BAS to produce a seamless and real-time energy-related information exchange system. The proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity.

Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at limited number of homes or an aggregate load of a collection of homes. This study aims to address this gap and serve as an investigation on selecting the better deep learning model architecture for short term load forecasting on 3 communities of residential buildings. The deep learning models CNN and LSTM have been used in the study. For 15-min ahead forecasting for a collection of homes it was found that homes with a higher variance were better predicted by using CNN models and LSTM showed better performance for homes with lower variances. The effect of adding weather variables on 24-hour ahead forecasting was studied and it was observed that adding weather parameters did not show an improvement in forecasting performance. In all the homes, deep learning models are shown to outperform the simple ANN model. / Master of Science / Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at only a single home or an aggregate load of a collection of homes. This study aims to address this gap and serve as an analysis on short term load forecasting on 3 communities of residential buildings. Detailed analysis on the model performances across all homes have been studied. Deep learning models have been used in this study and their efficacy is measured compared to a simple ANN model.

Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions. / M.S. / The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle on average. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.

With a nation-wide aim toward reducing operational energy costs in buildings, it is important to understand the dynamics of controlled heating, cooling, and air circulation of an individual room, the "One-Room Model Problem." By understanding how one most efficiently regulates a room's climate, one can use this knowledge to help develop overall best-practice power reduction strategies. A key toward effectively analyzing the "One-Room Model Problem" is to understand the capabilities and limitations of existing commercial tools designed for similar problems. In this thesis we develop methodology to link commercial Computational Fluid Dynamics (CFD) software COMSOL with standard computational mathematics software MATLAB, and design controllers that apply inlet airflow and heating or cooling to a room and investigate their effects. First, an appropriate continuum model, the Boussinesq System, is described within the framework of this problem. Next, abstract and weak formulations of the problem are described and tied to a Finite Element Method (FEM) approximation as implemented in the interface between COMSOL and MATLAB. A methodology is developed to design Linear Quadratic Regulator (LQR) controllers and associated functional gains in MATLAB which can be implemented in COMSOL. These "closed-loop" methods are then tested numerically in COMSOL and compared against "open-loop" and average state closed-loop controllers. / Ph. D.

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