Exegesis of building systems through the lens of systems theory: a conceptual paper

Commercial heating, ventilation and air conditioning (HVAC ) system consumes large portion of the building energy use. With the abundance of the available data in the building automation systems (BAS) of commercial buildings, ample opportunities have emerged to help develop adequate data-driven control of HVAC systems. This paper proposes the use of data-driven reinforcement learning (RL) that can evaluate control policies and develop new ones. A Q- learning algorithm is used as a type of reinforcement learning to minimize the building energy consumption cost while maintaining the comfort level. The proposed Q-learning algorithm is trained using actual data where the data is first used to develop temperature and energy models. Four different machine learning methodologies are used to obtain these models which are linear regression, deep neural network, support vector machines and random forests. The performance of the Q- learning algorithm under each methodology is tested and compared with the others. The algorithm is validated using a decent physics-based model of a 3-floor office/classroom building. The results showed that though all the four methodologies yield satisfactory results, the Q-learning algorithm performed the best under support vector machine and random forest.

In code-compliant simulations, the impact of thermal zoning method is significant on building energy use of building systems. However, in practice, thermal zone modeling is often determined by general rules without careful consideration of the specific characteristics of different HVAC systems. Therefore, this study evaluated the impact of thermal zone modeling on all-electric HVAC Systems in hot-humid and cold-humid climates using different levels of usage profiles. The results of this study identify the significant influence of thermal zoning methods on the annual energy use in all-electric HVAC systems depending on usage rates and climate. When different thermal zoning models were used, the amount of energy used differed depending on HVAC system type and zone usage (i.e., PSZ system varied from 4.6% to 8.8% in Houston, TX, and from 4.6% to 20.1% in Chicago, IL). In addition, a significant difference can be found in peak heating demands for selected HVAC systems in buildings located in cold climates (i.e., for winter peak days, the daily load differences of 21.8% to 24.5% in Chicago, IL). The results of this study can be used to improve the accuracy of thermal zone modeling for code compliance that considers different HVAC system types.

Development of a methodology for life cycle building energy ratings

Ten years ago, the primary author developed the Building Energy-Efficient Hive (BEEHive) concept in order to demonstrate in theory that environmental design – which is aimed at addressing environmental parameters – can support the design and operation of energy-efficient office buildings. This was a result of his analysis of the spheroid form’s efficiency in nature, and his development of a spheroid-like energy-efficient office built form. The BEEHive incorporates environmental design principles such as: site considerations; built form; ventilation strategy; daylighting strategy; and services strategy. Furthermore, several notable environmental design advocates and practitioners have made significant contributions in order to improve building performance. However, in practice environmental design has had limited success in the attainment of balance and optimisation in all aspects of energy use; hence there is typically a gap between predicted and actual office building energy use. The primary author’s previous study established the impacts of contributory factors in the gap between predicted and actual office building energy use. It has contributed to this current study, which is also a part of the primary author’s doctor of philosophy (Ph.D) research, and it has established the role of a key contributory factor, that is, the role of building energy and environmental assessment in facilitating office building energy-efficiency. It involved a combination of literature reviews, multiple case study research and comparative studies in order to build theory. It also established the methods and tool to be used in the primary author’s Ph.D research for multiple case studies and simulation studies of office building energy-efficiency. Analysis of the literature revealed that the role of building energy and environmental assessment involves assessment of the impacts of environmental design principles, and the impacts of factors that contribute to office building energy use gap decreases, for example: solar gain minimisation orientations; energy-efficient strategies for built forms, ventilation, lighting and services; and decreases in hours of operation and occupancy. Its role also involves assessment of the impacts of factors that contribute to office building energy use gap increases, for example: weather variation and microclimates; and increases in hours of operation and occupancy. There are three key types of building energy and environmental assessment, and these are: building energy use audit method; building energy simulation analysis method and tools; and building energy and environmental assessment rating method and tools. Their respective roles include: tracking building energy use over time; predicting future building energy use within multiple environmental design scenarios and parameters; and assessing, rating, and certifying building energy and environmental efficiency. However, limitations of building energy and environmental assessment, and impacts of factors that contribute to office building energy use gap increases need to be addressed in order to achieve: optimum building energy use assessments and predictions; optimum environmental design principles; and building energy use gap decreases for improved energy performance. This study has contributed to ideas for the development of a Building Management System (BMS-Optimum) for Bridging the Gap, which is comprised of optimum conditions and considerations such as: optimum environmental design principles; optimum weather and microclimate considerations; accessibility to reliable office building energy use data; optimum building energy and environmental assessment; optimum hours of operation; and optimum level and nature of occupancy. Future work will include further development of BMS-Optimum, using methods such as: multiple case study research supported by building energy use audits, observations, questionnaire surveys, interviews, benchmarking and comparative studies; building energy simulations within multiple scenarios, parameters and variables, and supported by benchmarking and comparative studies; and peer reviews and focus group sessions. These will also help establish and validate a Framework for Improved Environmental Design and Energy Performance (FEDEP).

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance–in terms of occupant comfort and building energy use–degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5 to 20% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.

Theoretical Minimum Thermal Load in Buildings

The increasing availability and affordability of wireless building and home automation networks has increased interest in residential and commercial building energy management. This interest has been coupled with an increased awareness of the environmental impact of energy generation and usage. Residential appliances and equipment account for 30% of all energy consumption in OECD countries and indirectly contribute to 12% of energy generation related carbon dioxide (CO2) emissions (International Energy Agency, 2003). The International Energy Association also predicts that electricity usage for residential appliances would grow by 12% between 2000 and 2010, eventually reaching 25% by 2020. These figures highlight the importance of managing energy use in order to improve stewardship of the environment. They also hint at the potential gains that are available through smart consumption strategies targeted at residential and commercial buildings. The challenge is how to achieve this objective without negatively impacting people’s standard of living or their productivity. The three primary purposes of building energy management are the reduction/management of building energy use; the reduction of electricity bills while increasing occupant comfort and productivity; and the improvement of environmental stewardship without adversely affecting standards of living. Building energy management systems provide a centralized platform for managing building energy usage. They detect and eliminate waste, and enable the efficient use electricity resources. The use of widely dispersed sensors enables the monitoring of ambient temperature, lighting, room occupancy and other inputs required for efficient management of climate control (heating, ventilation and air conditioning), security and lighting systems. Lighting and HVAC account for 50% of commercial and 40% of residential building electricity expenditure respectively, indicating that efficiency improvements in these two areas can significantly reduce energy expenditure. These savings can be made through two avenues: the first is through the use of energy-efficient lighting and HVAC systems; and the second is through the deployment of energy management systems which utilize real time price information to schedule loads to minimize energy bills. The latter scheme requires an intelligent power grid or smart grid which can provide bidirectional data flows between customers and utility companies. The smart grid is characterized by the incorporation of intelligenceand bidirectional flows of information and electricity throughout the power grid. These enhancements promise to revolutionize the grid by enabling customers to not only consume but also supply power.

Warming impact on energy use of HVAC system in buildings of different thermal qualities and in different climates

The role of passive, active, and operational parameters in the relationship between urban heat island effect (UHI) and building energy consumption

A review on buildings energy information: Trends, end-uses, fuels and drivers

Ten years ago, the primary author developed the Building Energy-Efficient Hive (BEEHive) concept in order to demonstrate in theory that environmental design – which is aimed at addressing environmental parameters – can support the design and operation of energy-efficient office buildings. This was a result of his analysis of the spheroid form’s efficiency in nature, and his development of a spheroid-like energy-efficient office built form that encloses and shades the most volume of office space with the least surface area possible. The BEEHive concept also incorporates several other aspects of the environmental design philosophy, including: site considerations (location and weather, microclimate, site layout and orientation); built form (shape, thermal response, insulation and windows/glazing); ventilation strategy; daylighting strategy; and services strategy (plants and controls, fuels and metering). Furthermore, several notable environmental design advocates and practitioners have made significant contributions in order to improve building performance. However, in practice environmental design has had limited success in the attainment of balance and optimisation in all aspects of energy use; hence there is typically a gap between predicted and actual office building energy use. This study has established the impacts of contributory factors in the gap between predicted and actual office building energy use, and it is a part of the primary author’s doctor of philosophy (Ph.D) research. It involved a combination of literature reviews, multiple case study research and comparative studies in order to build theory, and it established the reasons for the gap, as well as the best ways to bridge it for improved office building environmental design and energy performance. Analysis of the literature revealed two types of gaps, and these are a gap increase and a gap decrease, which are among the impacts attributable to contributory factors in the gap between predicted and actual office building energy use such as: the nature of environmental design measures implemented; weather variation and microclimates; unavailability of reliable building energy use data; limitations of building energy simulation software; level of hours of operation; and level and nature of occupancy. Amongst these, the key contributors to gap increases are increases in hours of operation and occupancy, and weather variation and microclimates. Their respective major impacts are discrepancy between predicted and actual hours of operation and increased energy use, increased heat output and uncertainties, and variable heating and cooling requirements. The key contributors to gap decreases are environmental design measures such as the use of: natural ventilation strategies; daylighting strategies; solar photovoltaic systems; and spheroid-like built forms. Their respective major impacts are: the production of more energy than an office building uses; and energy uses that are below, for instance, Energy Consumption Guide 19 typical and good practice energy use for office type 4, and relevant ASHRAE (American Society of Heating, Refrigerating and Air Conditioning Engineers) standards. This study has contributed to ideas for the development of a Building Management System for Bridging the Gap, otherwise known as ‘BMS-Optimum’, which is comprised of optimum conditions and considerations such as: optimum environmental design principles; optimum weather and microclimate considerations; accessibility to reliable office building energy use data; optimum building energy and environmental assessment; optimum hours of operation; and optimum level and nature of occupancy. Future work will include further development of BMS-Optimum, using methods such as: multiple case study research supported by building energy use audits, observations, questionnaire surveys, interviews, benchmarking and comparative studies; building energy simulations within multiple scenarios, parameters and variables, and supported by benchmarking and comparative studies; and peer reviews and focus group sessions. These will also help establish and validate a Framework for Improved Environmental Design and Energy Performance (FEDEP).

Weather normalization is a crucial step in building energy rating and retrofit measurements. Accounting for the impacts of weather on energy use of commercial buildings is a rigorous challenge because of the complexity and diversity in the operation, the mechanical systems, and the use-types available. This paper documents preliminary results of an effort to determine a set of weather adjustment coefficients that can be used to isolate the impacts of weather on energy use of buildings in 1020 weather location sites available in the U.S. The U.S. Department of Energy (DOE) commercial reference building models are adopted as hypothetical models with standard operations to deliver consistency in modeling. The correlation between building envelope design, heating, ventilation and air conditioning (HVAC) system design and properties for different building types and the change in heating and cooling energy consumption caused by variations in weather is examined.

In the early design phase of a building, the task of the Heating, Ventilation and Air Conditioning (HVAC) engineer is to propose an appropriate HVAC system for a given building. This system should provide thermal comfort to the building occupants at all time, meet the building owner’s specific requirements, and have minimal investment, running, maintenance and replacement costs (i.e., the total cost) and energy use or environmental impact. Calculating these different aspects is highly time-consuming and the HVAC engineer will therefore only be able to compare a (very) limited number of alternatives leading to suboptimal designs. This study presents therefore a Python tool that automates the generation of all possible scenarios for given thermal power profiles and energy load and a given database of HVAC components. The tool sizes each scenario properly, computes its present total cost (PC) and the total CO 2 emissions associated with the building energy use. Finally, the different scenarios can be searched and classified to pick the most appropriate scenario. The tool uses static calculations based on standards, manufacturer data and basic assumptions similar to those made by engineers in the early design phase. The current version of the tool is further focused on hybrid GEOTABS building, which combines a GEOthermal heat pump with a Thermally Activated System (TABS). It should further be noted that the tool optimizes the HVAC system but not the building envelope, while, ideally, both should be simultaneously optimized.

Occupant behavior in buildings is one of the key drivers of building energy performance. Closing the gap in the building sector requires a deeper understanding and consideration of the in energy usage. For Europe and US to meet their challenging 2020 and 2050 energy and GHG reduction goals, we need to harness the potential savings of human behavior in buildings, in addition to deployment of energy efficient technologies and energy policies for buildings. Through involvement in international projects such as IEA ECBC Annex 53 and EBC Annex 66, the research conducted in the context of this thesis provided significant contributions to understand occupants' interactions with building systems and to reduce their energy use in residential and commercial buildings over the entire building life cycle. The primary goal of this Ph.D. study is to explore and highlight the human factor in energy use as a fundamental aspect influencing the energy performance of buildings and maximizing energy efficiency - to the same extent as technological innovation. Scientific literature was reviewed to understand state-of-the-art gaps and limitations of research in the field. Human energy-related behavior in buildings emerges a stochastic and highly complex problem, which cannot be solved by one discipline alone. Typically, a technological-social dichotomy pertains to the human factor in reducing energy use in buildings. Progressing past that, this research integrates occupant behavior in a multidisciplinary approach that combines insights from the technical, analytical and social dimension. This is achieved by combining building physics (occupant behavior simulation in building energy models to quantify impact on building performance) and data science (data mining, analytics, modeling and profiling of behavioral patterns in buildings) with behavioral theories (engaging occupants and motivating energy-saving occupant behaviors) to provide multidisciplinary, innovative insights on human-centered energy efficiency in buildings. The systematic interconnection of these three dimensions is adopted at different scales. The building system is observed at the residential and commercial level. Data is gathered, then analyzed, modeled, standardized and simulated from the zone to the building level, up to the district scale. Concerning occupant behavior, this research focuses on individual, group and collective actions. Various stakeholders can benefit from this Ph.D. dissertation results. Audience of the research includes energy modelers, architects, HVAC engineers, operators, owners, policymakers, building technology vendors, as well as simulation program designers, implementers and evaluators. The connection between these different levels, research foci and targeted audience is not linear among the three observed systems. Rather, the multidisciplinary research approach to energy-related behavior in buildings proposed by this Ph.D. study has been adopted to explore solutions that could overcome the limitations and shortcomings in the state-of-the-art research

Heating, ventilation, and air conditioning (HVAC) systems are responsible for maintaining occupants’ thermal comfort and share a large portion of the overall building energy use. Hence, it is of great interest to improve the performance of HVAC control systems and thus the building energy efficiency. Model predictive control (MPC) has been proved to be a promising control strategy to be employed in this field. However, MPC implementation relies on the model of the system, and inaccurate models can deteriorate the control performance, while overly complicated models can lead to the prohibitive computational burden. Because of this, existing models do not usually allow the MPC controller to adjust multiple set points (e.g., both temperature and flow rates) and do not include the dynamics of the heating and ventilation subsystems with their local controllers. In this paper, we address the challenge of developing more reliable HVAC models for MPC controllers based on the experimental data. Data are obtained from an experiment designed using a graph theoretical technique, which guarantees maximum information content in the data. The resulting models are employed to design local controllers of the heating and ventilation subsystems, which are experimentally tested in a real HVAC test bed. A supervisory MPC controller that incorporates the closed-loop models of the heating and ventilation subsystems is then developed. This can lead to a control strategy able to more effectively adapt key HVAC set points based on weather conditions, occupancy, and actual thermal comfort, as shown by a numerical study based on the data from the HVAC test bed.