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This dissertation explores the idea of inducing grid peak demand curtailment by turning commercial buildings into interactive assets for building owners during the demand control period. The work presented here is useful for both ab initio design of new sites and for existing or retrofitted sites. An analytical hierarchy process (AHP)-based framework is developed to curtail the thermal load effectively across a group of commercial buildings. It gives an insight into the amount of peak demand reduction possible for each building, subject to indoor thermal comfort constraints as per ASHRAE standards. Furthermore, the detailed operation of buildings in communion with the electric grid is illustrated through case studies. This analysis forms an outline for the assessment of transactive energy opportunities for commercial buildings in distribution system operations and lays the foundation for a seamless building-to-grid integration framework. The contribution of this dissertation is fourfold – (a) an efficient method of developing high-fidelity physics-based building energy models for understanding the realistic operation of commercial buildings, (b) identification of minimal dataset to achieve a target accuracy for the building energy models (c) quantification of building peak demand reduction potential and corresponding energy savings across a stipulated range of thermostat setpoint temperatures and (d) AHP-based demand curtailment scheme. By careful modeling, it is shown that commercial building models developed using this methodology are both accurate and robust. As a result, the proposed approach can be extended to other commercial buildings of diverse characteristics, independent of the location. The methodology presented here takes a holistic approach towards building energy modeling by accounting for several building parameters and interactions between them. In addition, parametric analysis is done to identify a useful minimal dataset required to achieve a specified accuracy for the building energy models. This thesis describes the concept of commercial buildings as interactive assets in a transactive grid environment and the idea behind its working. / Doctor of Philosophy / This dissertation titled "An Expert-based Approach for Grid Peak Demand Curtailment using HVAC Thermostat Setpoint Interventions in Commercial Buildings" tackles two important challenges in the energy management domain: –electric grid peak demand curtailment and energy savings in commercial buildings. The distinguishing feature of the proposed solution lies in addressing these challenges solely through demand-side management (DSM) strategies, which include HVAC thermostat setpoint interventions and lighting control. We present a methodology for developing highly accurate building energy models that serve as digital twins of actual buildings. These digital replicas can be used to quantify the impact of various interventions and reflect the realistic operation of commercial buildings across varied conditions. This enables building owners to control demand intelligently and transact energy effectively in the electricity market. The development of Internet of Things (IoT) market and advanced technologies such as smart meters and smart thermostats allows for the design of novel strategies that address traditional challenges faced by electric grid operators. This dissertation elaborates on how smart buildings can leverage IoT-based solutions to participate in the electricity market during demand control periods. We also developed an expert opinion-based demand curtailment allocation scheme resulting in grid peak demand reduction. The numerical results obtained reinforce the effectiveness of the proposed solution across varied climatic conditions.

The goal of this dissertation is to demonstrate that data from a remotely located building can be utilized for energy modeling of a similar type of building and to demonstrate how to use this remote data without physically moving the data from one server to another using Virtual Information Fabric Infrastructure (VIFI). In order to achieve this goal, firstly an EnergyPlus model was created for Greek Life Center, a campus building located at University of North Texas campus at Denton in Texas, USA. Three thermal comfort models of Fanger model, Pierce two-node model and KSU two-node model were compared in order to find which one of these three models is most accurate to predict occupant thermal comfort. This study shows that Fanger's model is most accurate in predicting thermal comfort. Secondly, an experimental data pertaining to lighting usage and occupancy in a single-occupancy office from Carnegie Mellon University (CMU) has been implemented in order to perform energy analysis of Greek Life Center assuming that occupants in this building's offices behave similarly as occupants in CMU. Thirdly, different data types, data formats and data sources were identified which are required in order to develop a city-scale urban building energy model (CS-UBEM). Two workflows were created, one for an individual scale building energy model and another one for CS-UBEM. A new innovative infrastructure called as Virtual Information Fabric Infrastructure (VIFI) has been introduced in this dissertation. The workflows proposed in this study will demonstrate in the future work that by using VIFI infrastructure to develop building energy models there is a potential of using data for remote servers without actually moving the data. It has been successfully demonstrated in this dissertation that data located at remote location can be used credibly to predict energy consumption of a newly built building. When the remote experimental data of both lighting and occupancy are implemented, 4.57% energy savings was achieved in the Greek Life Center energy model.

In order to achieve global carbon reduction targets, buildings must be designed to be energy efficient. Building performance simulation methods, together with sensitivity analysis and evolutionary optimization methods, can be used to generate design solution and performance information that can be used in identifying energy and cost efficient design solutions. Sensitivity analysis is used to identify the design variables that have the greatest impacts on the design objectives and constraints. Multi-objective evolutionary optimization is used to find a Pareto set of design solutions that optimize the conflicting design objectives while satisfying the design constraints; building design being an inherently multi-objective process. For instance, there is commonly a desire to minimise both the building energy demand and capital cost while maintaining thermal comfort. Sensitivity analysis has previously been coupled with a model-based optimization in order to reduce the computational effort of running a robust optimization and in order to provide an insight into the solution sensitivities in the neighbourhood of each optimum solution. However, there has been little research conducted to explore the extent to which the solutions found from a building design optimization can be used for a global or local sensitivity analysis, or the extent to which the local sensitivities differ from the global sensitivities. It has also been common for the sensitivity analysis to be conducted using continuous variables, whereas building optimization problems are more typically formulated using a mixture of discretized-continuous variables (with physical meaning) and categorical variables (without physical meaning). This thesis investigates three main questions; the form of global sensitivity analysis most appropriate for use with problems having mixed discretised-continuous and categorical variables; the extent to which samples taken from an optimization run can be used in a global sensitivity analysis, the optimization process causing these solutions to be biased; and the extent to which global and local sensitivities are different. The experiments conducted in this research are based on the mid-floor of a commercial office building having 5 zones, and which is located in Birmingham, UK. The optimization and sensitivity analysis problems are formulated with 16 design variables, including orientation, heating and cooling setpoints, window-to-wall ratios, start and stop time, and construction types. The design objectives are the minimisation of both energy demand and capital cost, with solution infeasibility being a function of occupant thermal comfort. It is concluded that a robust global sensitivity analysis can be achieved using stepwise regression with the use of bidirectional elimination, rank transformation of the variables and BIC (Bayesian information criterion). It is concluded that, when the optimization is based on a genetic algorithm, that solutions taken from the start of the optimization process can be reliably used in a global sensitivity analysis, and therefore, there is no need to generate a separate set of random samples for use in the sensitivity analysis. The extent to which the convergence of the variables during the optimization can be used as a proxy for the variable sensitivities has also been investigated. It is concluded that it is not possible to identify the relative importance of variables through the optimization, even though the most important variable exhibited fast and stable convergence. Finally, it is concluded that differences exist in the variable rankings resulting from the global and local sensitivity methods, although the top-ranked solutions from each approach tend to be the same. It also concluded that the sensitivity of the objectives and constraints to all variables is obtainable through a local sensitivity analysis, but that a global sensitivity analysis is only likely to identify the most important variables. The repeatability of these conclusions has been investigated and confirmed by applying the methods to the example design problem with the building being located in four different climates (Birmingham, UK; San Francisco, US; and Chicago, US).

This thesis presents the design and implementation of control strategies for building energy management systems (BEMS). The controllers considered include the multi PI-loop controllers, state-space designs, constrained input and output MIMO adaptive controllers, fuzzy logic solutions and genetic algorithm techniques. The control performances of the designs developed using the various methods based on aspects such as regulation errors squared, energy consumptions and the settling periods are investigated for different designs. The aim of the control strategy is to regulate the room temperature and the humidity to required comfort levels. In this study the building system under study is a 3 input/ 2 output system subject to external disturbances/effects. The three inputs are heating, cooling and humidification, and the 2 outputs are room air temperature and relative humidity. The external disturbances consist of climatic effects and other stochastic influences. The study is carried out within a simulation environment using the mathematical model of the test room at Loughborough University and the designed control solutions are verified through experimental trials using the full-scale BMS facility at the University of Bradford.

Energy efficiency in the operation of buildings is becoming increasingly important with a growing emphasis on sustainability and reducing environmental impacts of irresponsible energy usage. Improvements have been made both on the technology side of energy efficiency and on the human behavior side. However, when changing human behavior, it is critical to find energy conservation measures that will maintain comfort for occupants. This paper analyzes how this can be done by implementing a modulating temperature schedule based on the concept of alliesthesia, which states that pleasure is observed in transient states. EnergyPlus simulations were used to show that in cooling applications, this type of scheduling can produce significant energy savings. However, energy savings are not predicted for the same type of scheduling for heating applications. Thermal comfort was examined with a cooling experiment and a separate heating experiment, each lasting 45 minutes and taking place during the corresponding season. The experiments showed that modulating temperatures can cause occupants to experience more pleasure than if the temperature remained constant in a cooled space, whereas modulating temperatures had a negative impact on comfort relative to the constant temperature in the heated space. This presents evidence for an ideal opportunity for cooling applications by implementing modulating temperature schedules: an increase in thermal pleasure accompanied by a decrease in cooling energy.

Les écarts communément observés entre les prévisions de consommations énergétiques et les performances réelles des bâtiments limitent le développement des projets de construction et de réhabilitation. La garantie de performance énergétique (GPE) a pour vocation d’assurer des niveaux de consommations maximaux et donc de sécuriser les investissements. Sa mise en place fait cependant face à plusieurs problématiques, notamment techniques et méthodologiques. Ces travaux de thèse se sont intéressés au développement d’une méthodologie pour la GPE associant les outils de simulation énergétique dynamique (SED) à un protocole de mesure et vérification. Elle repose d’abord sur la modélisation physico-probabiliste du bâtiment. Les incertitudes sur les paramètres physiques et techniques, et les variabilités des sollicitations dynamiques sont modélisées et propagées dans la SED. Un modèle de génération de données météorologiques variables a été développé. L’étude statistique des résultats de simulation permet d’identifier des modèles liant les consommations d’intérêt à des facteurs d’ajustement, caractéristiques des conditions d’exploitation. Les méthodes de régression quantile permettent de déterminer le quantile conditionnel des distributions et caractérisent donc conjointement la dépendance aux facteurs d’ajustement et le niveau de risque de l’engagement. La robustesse statistique de ces méthodes et le choix des meilleurs facteurs d’ajustement ont été étudiés, tout comme l’influence des incertitudes sur la mesure des grandeurs d’ajustement en exploitation. Leur impact est intégré numériquement en amont de la méthodologie. Cette dernière est finalement mise en œuvre sur deux cas d’étude : la rénovation de logements, et la construction de bureaux. / Discrepancies between ex-ante energy performance assessment and actual consumption of buildings hinder the development of construction and renovation projects. Energy performance contracting (EPC) ensures a maximal level of energy consumption and secures investment. Implementation of EPC is limited by technical and methodological problems.This thesis focused on the development of an EPC methodology that allies building energy simulation (BES), and measurement and verification (M&V) process anticipation. The building parameters’ uncertainties and dynamic loads variability are considered using a Monte-Carlo analysis. A model generating synthetic weather data was developed. Statistical studies of simulation results allow a guaranteed consumption limit to be evaluated according to a given risk. Quantile regression methods jointly capture the risk level and the relationship between the guaranteed energy consumption and external adjustment factors. The statistical robustness of these methods was studied as well as the choice of the best adjustment factors to consider. The latter will be measured during building operation. The impact of measurement uncertainties is statistically integrated in the methodology. The influence of M&V process accuracy is also examined. The complete EPC methodology is finally applied on two different projects: the refurbishment of a residential building and the construction of a high energy performance office building.

Low-emissivity (low-E) window films are designed to improve the energy performance of windows and prevent indoor overheating by solar radiation. These films can be applied to different types of glazing units without the need for changing the whole window. This characteristic offers the possibility to improve the energy performance of the window of old and historic buildings for which preservation regulations say windows should remain more or less unchanged. This research aims to figure out to what extent a low-E window film can improve thermal comfort and energy performance of an old three-storey historic stone building in the cold climate of Mid-Sweden. In this research, first, with help of the simulation software “IDA ICE”, the entire building was modelled without window films in a one-year simulation. Second step was to add the low-E window films (3M Thinsulate Climate Control 75 (CC75)) to all the windows and repeat the simulation. Comparison between the results of the two cases revealed an improvement in energy use reduction as well as the thermal comfort when applying the films. For the application of the window films, a cost analysis using payback method was carried out which showed a long- time payback period. Although an investment with a long-time payback period is considered as a disadvantage, for historic buildings with very strict retrofit regulations specially when it comes to the building’s facades, application of the low-emissivity window films for better energy performance and thermal comfort is among the recommendable measures, but not necessarily the best.

Le secteur du bâtiment, responsable de plus de 40% de consommation d’énergie globale et un tiers des émissions de gaz à effet de serre mondial, est un des centres de préoccupations autour des sujets liés au changement climatique et l’indépendance énergétique. Le travail de recherche a exigé l’apport de connaissances supplémentaires et la création d’outils spécifiques orientés sur l’optimisation globale du management énergétique des bâtiments de type tertiaire. Une problématique industrielle est associée à ces enjeux de transitions énergétique et écologique, à savoir le frein observé à la mise en place de plans d’actions de rénovation. En effet, pour des opérations d’optimisation ou de rénovation de petites-moyennes envergures, les coûts initiaux d’études et de métrologie représentent plus de 50% de leur coût global. Cette mise de fonds induit un retour sur investissement très long. Face à ce paramètre financier prohibitif, beaucoup d’entreprises sont réticentes à mettre en place ce type d’action. L’objectif opérationnel a donc été de proposer une solution permettant de réduire drastiquement ces coûts préliminaires.Les aspects abordés dans la thèse sont : l’état de l’art du fonctionnement du bâtiment et des enjeux associés, la création d’un outil de collecte et de remontée des données de fonctionnement et de performance du bâtiment grâce à un réseau de mesure in-situ dédié, concomitant à l’élaboration d’un modèle thermique simplifié adjoint facilitant la compréhension de son comportement, puis l’identification de ses paramètres "observables" de conception et de fonctionnement par méthode inverse, et enfin le calcul de sa consommation énergétique optimale grâce à une méthode d’optimisation. Plus spécifiquement, l’approche sera orientée vers le développement d’outils pour promouvoir un accès facilité à la réduction des consommations unitaires auprès des entreprises au niveau national et l’intégration d’une intelligence pour l’optimisation énergétique des éléments climatiques du bâtiment ou son usage, ou encore une interface ergonomique homme-machine permettant un management efficace de son fonctionnement. Dans les faits, le problème observé est holistique et ne peut pas être pris en compte de manière sectorielle. Il est impératif d’y intégrer tous les processus impliqués dans le bâtiment et son usage (aspect comportemental des usagers). L’approche utilisée a été orientée afin de prendre en compte ultérieurement des paramètres autres que strictement énergétique, tel que les coûts ou le confort / The building field is responsible of about 40% of global energy consumption and a third of world greenhouse gas emissions. It is a main concern subject in climate change issues and fossil fuel independency. The aim of the PhD work is to bring more knowledge about thermal modeling and to create specific tools which are capable of globally optimize the office building energy management. The industrial purpose is associated with its area of expertise, which is advice in energy and ecologic transition. It concerns the difficulty to implement a retrofit action planning. Indeed, for small or middle retrofit actions, the initial study and metrology costs represent over 50% of the overall cost. This down payment induces a long return of investment. Faced with this prohibitive financial parameter, a lot of companies are reluctant to implement this type of actions. The proposed purpose is a solution that drastically reduces preliminary costs. The aspects addressed in this thesis are: the building operation state of art and its associated issues, the creation of reporting and collecting data tool of building operation and performance thanks to a dedicated in-situ measurement network, concomitant with the development of a simplified adjoin thermal model. It facilitates the understanding of its behavior. Then the final aspect are the two steps of optimization. The first is the observable building design and operation parameters with an inverse method, the second is the calculation of optimal energy consumptions. The approach is specifically oriented through the development of tools allowing a facilitated access to energy reduction action for national companies. This should assist the integration of an intelligence for energy optimization for building climatics and thermal equipments or usage. The result could be a new ergonomic man-machine interface for stock building effective management. In the facts, the problem is holistic and cannot be handle sectorally. It is imperative to integrate all the process involved in the building and its use (user behavior). The approach have been oriented to take later into account other parameters than strictly energy, as costs of comfort

This thesis details the ways in which energy is consumed in an existing Canadian high-rise apartment building and outlines a strategy to reduce its consumption of grid purchased energy by 90%. Grid purchased energy is targeted because the building is located in Saskatchewan where energy is predominantly generated from fossil fuels that release greenhouse gas emissions into the environment. Greenhouse gas emissions are targeted because of the growing consensus that human activities are the cause of recent global climate destabilization and the general trend towards global warming. Energy consumption is also a concern because of anticipated resource shortages resulting from increases in both global population and average per capita consumption. Many researchers are beginning to claim that a factor 10 reduction in energy use by industrialized nations will be required in order for our civilization to be sustainable.<p>The building that was studied is an 11 story seniors high-rise with a total above ground floor area of 8,351 m2. It was constructed in 1985, in Saskatoon, SK, and it is an average user of energy for this region of the world and for a building of its size and type. Numerous field measurements were taken in the building, both during this study and previously by the Saskatchewan Research Council. These measurements were used to create a computer model of the building using EE4. After the computer model of the building was created different energy saving retrofits were simulated and compared. <p>Over 40 retrofits are presented and together they reduce the annual grid purchased energy of the building from 360 kWh/m2 (based on above ground floor area) to 36 kWh/m2, a factor 10 reduction. Natural gas consumption was reduced by approximately 94% and grid purchased electrical consumption was reduced by approximately 81%. As a result of these energy savings, a factor 6.6 reduction (85%) in greenhouse gas emissions was also achieved. The goal of factor 10 could not be achieved only through energy conservation and the final design includes two solar water heating systems and grid-connected photovoltaic panels. These systems were modeled using RETScreen project analysis tools.<p>Capital cost estimates and simple payback periods for each retrofit are also presented. The total cost to retrofit the building is estimated to be $3,123,000 and the resulting utility savings from the retrofits are approximately $150,000 per year. This is a factor 6.0 reduction (83%) in annual utility costs in comparison to the base building. While the typical response to proposing a green building is that financial sacrifices are required, there is also research available stating that operating in a more sustainable manner is economically advantageous. This research project adds to the green building economics debate by detailing savings and costs for each retrofit and ranking each retrofit that was proposed. The most economically advantageous mechanical system that was added to the building was energy recovery in the outdoor ventilation air. It should also be noted that there was already a glycol run-around heat recovery system in the building and even greater savings would have been obtained from installing the energy recovery system had this not been the case.<p>While the goal of factor 10 required economically unjustifiable retrofits to be proposed, the majority of the retrofits had simple payback periods of less than 20 years (30 out of 49). This research shows that certain retrofits have highly desirable rates of return and that when making decisions regarding investing in auditing a building, improving energy efficiency, promoting conservation, or utilizing renewable energy technologies, maintaining the status quo may be economically detrimental. This would be especially true in the case of new building construction.