Residential buildings in the United States are responsible for the consumption of 38% of electricity, and for much of the fluctuations in the power demands on the electric grid, particularly in hot climates. Residential buildings are also where occupants spend nearly 69% of their time. As “smart” technologies, including electric grid-connected devices and home energy management systems are increasingly available and installed in buildings, this research focuses on the use of these technologies combined with available energy use data in accomplishing three main objectives. The research aims to: (a) better understand how residential buildings currently use electricity, (b) evaluate the use of these smart technologies and data to reduce buildings’ electricity use and their contribution to peak loads, and (c) develop a methodology to assess the impacts of these operational changes on occupant thermal comfort. Specifically this study focuses on two of the most significant electricity consumers in residential buildings: large appliances, including refrigerators, clothes washers, clothes dryers and dishwashers, and heating, ventilation and air conditioning (HVAC) systems. First, to develop an improved understanding of current electricity use patterns of large appliances and residential HVAC systems, this research analyzes a large set of field-collected data. This dataset includes highly granular electricity consumption information for residential buildings located in a hot and humid climate. The results show that refrigerators have the most reliable and consistent use, while the three user-dependent appliances varied more greatly among houses and by time-of-day. In addition, the daily use patterns of appliances vary in shape depending on a number of factors, particularly whether or not the occupants work from home, which contrasts with common residential building energy modeling assumptions. For the all-air central HVAC systems studied, the average annual HVAC duty cycle was found to be approximately 20%, and varied significantly depending on the season, time of day, and type of residential building. Duty cycle was also correlated to monthly energy use. This information provides an improvement to previously assumed values in indoor air modeling studies. Overall, the work presented here enhances the knowledge of how the largest consumers of residential buildings, large appliances and HVAC, operate and use energy, and identifies influential factors that affect these use patterns. The methodologies developed can be applied to determine use patterns for other energy consuming devices and types of buildings, to further expand the body of knowledge in this area. Expanding on this knowledge of current energy use, smart large appliances and residential HVAC systems are investigated for use in reducing peak electric grid loads, and building energy use, respectively. This includes a combination of laboratory testing, field-collected data, and modeling. For appliance peak load reduction, refrigerators are found to have a good demand response potential, in part due to the nearly 100% of residential buildings that have one or more of these appliances, and the predictability of their energy consumption behavior. Dryers provide less consistent energy use across all homes, but have a higher individual peak power demand during afternoon and evening peak use times. These characteristics also make dryers also a good candidate for demand response. The study of continuous commissioning of HVAC systems using energy data found that both runtime and energy use are increased, and cooling capacity and efficiency are reduced due to the presence of faults or inefficiencies. The correction of these faults have an estimated 1.4% to 5.7% annual impact on a residential building’s electricity use in a cooling-dominated climate such as the one studied. Overall, appliance peak load reduction results are useful for utility companies and policy makers in identifying what smart appliance may provide the most peak energy reduction potential through demand response programs. The results of the HVAC study provides a methodology that can be used with energy use data, to determine if an HVAC system has the characteristics implying an inefficiency may be present, and to quantify the annual savings resulting from its correction. The final aspect of this research focuses on the development of a tool to enable an assessment the effect of operational changes of a building associated with energy and peak load reduction on occupant comfort. This is accomplished by developing a methodology that uses the response surface methodology (RSM), combined with building performance data as input, and uncertainly analysis. A second-order RSM model constructed using a full-factorial design was generally found to provide strong agreement to in and out-of-sample building simulation data when evaluating the Average Percent of People Dissatisfied (PPD[subscript avg]). This 5-step methodology was applied to assess occupant thermal comfort in a residential building due to a 1-hour demand response event and a time-of-use pricing rate schedule for a variety of residential building characteristics. This methodology provides a model that can quickly assess, over a continuous range of values for each of the studied design variables, the effect on occupant comfort. This may be useful for building designers and operators who wish to quickly assess the effect of a change in building operations on occupants. / text

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.

Master of Science / Department of Architectural Engineering and Construction Science / Fred L. Hasler / The purpose of this report is to provide an introduction to the new ASHRAE Standard 189.1-2009, Standard for the Design of High-Performance Green Buildings. The report will include an overview of the standard to detail what the purpose, scope and requirements for high-performance buildings will be. The entire standard will be overviewed, but the focus of this paper is in the areas of energy efficiency and building performance. Next, the report will examine further impacts that the standard will have on the building design and construction industry. Chapter 3 includes the impact on other standards, specification writing and coordination of the design and construction teams. A case study of an office building is performed to compare a baseline building meeting ASHRAE Standard 90.1 to a building meeting the minimum standards of ASHRAE Standard 189.1. The case study compares the total annual energy use of the two projects to determine an expected energy savings. Based on this information, recommendations about the new standard will be discussed. Universities and government entities should require ASHRAE Standard 189.1 for new construction projects, to show willingness to provide sustainability in buildings. Finally, conclusions about how the standard will change and impact industry will be addressed. These conclusions will include issues with adopting ASHRAE Standard 189.1 as code as well as discussion on the LEED rating system.

To alleviate global warming, European-Union member states must reduce primary energy use, emit less carbon dioxide (CO2), and increase renewable energy use. Buildings constitute a great potential for energy savings, but saving energy in district-heated buildings influences combined heat and power (CHP) production, other electricity generation, and global CO2 emissions.   This thesis investigates the system effects from Swedish district heating production caused by district heating demand changes due to energy conservation in buildings. The cost-optimising linear programming modelling tools MODEST and FMS, the latter developed in the context of this thesis, are used to describe present district heating production and to investigate the impact of heat-demand reductions in twelve Swedish district heating systems, four of them representing all Swedish district heating.   Energy savings in district-heated, multi-family residential buildings yield a lower, more seasonally levelled district heating demand. These demand changes mainly reduce use of fossil-fuel and biomass for heat production. CHP production is significantly reduced if it supplies intermediate or peak district heating load. The αsystem value (ratio between generated CHP electricity and produced district heating) increases by demand reductions if CHP mainly supplies base district heating load. CO2 emissions due to district heat production depend on the approach used for CO2 assessment of electricity, and are generally reduced with heat demand reductions, unless the share of CHP production is large and the reduced fuel use yields smaller emission reductions than the emission increase from power production that replaces reduced CHP generation.   In total, heat demand reductions reduce CO2 emissions due to Swedish district heating, and the district heating systems even constitute a carbon sink at certain energy conservation levels. If saved biomass replaces fossil fuels elsewhere, a lower heat demand reduces CO2 emissions for every studied district heating system.

Reducing global energy consumption is a common challenge faced by the human race due to the energy shortage and growing energy demands. The building sector bears a large responsibility for the total energy consumption throughout the world. In particular, it was concluded that existing buildings, which are usually old and energy-inefficient, are the main reason for the high energy consumption of the building sector, in view of the low replacement rate (about 1%-3% per year) of existing buildings by new energy-efficient buildings. Therefore, improving the energy efficiency of existing buildings is a feasible and effective way to reduce energy consumption and mitigate the environmental impact of the building sector. The high energy intensity and requirements of a green building policy are the main motivation of this study, which focuses on finding cost-effective solutions to green building retrofit and maintenance planning to reduce energy consumption and ensure policy compliance. As about 50% of the total energy usage of a general building is caused by its envelope system, this study first proposes a multi-objective optimization approach for building envelope retrofit planning in Chapter 2. The purpose is to maximize the energy savings and economic benefits of an investment by improving the energy efficiency of existing buildings with the optimal retrofit plans obtained from the proposed approach. In the model formulation, important indicators for decision makers to evaluate an investment, including energy savings, net present value and the payback period, are taken into consideration. In addition, a photovoltaic (PV) power supply system is considered to reduce the energy demand of buildings because of the adequate solar resource in South Africa. The performance degradation of the PV system and corresponding maintenance cost are built into the optimization process for an accurate estimation of the energy savings and payback period of the investment so that decision makers are able to make informed decisions. The proposed model also gives decision makers a convenient way to interact with the optimization process to obtain a desired optimal retrofit plan according to their preferences over different objectives. In addition to the envelope system, the indoor systems of a general building also account for a large proportion of the total energy demand of a building. In the literature, research related to building retrofit planning methods aiming at saving energy examines either the indoor appliances or the envelope components. No study on systematic retrofit plan for the whole building, including both the envelope system and the indoor systems, has been reported so far. In addition, a systematic whole-building retrofit plan taking into account the green building policy, which in South Africa is the energy performance certificate (EPC) rating system, is urgently needed to help decision makers to ensure that the retrofit is financially beneficial and the resulting building complies with the green building policy requirements. This has not been investigated in the literature. Therefore, Chapter 4 of this thesis fills the above-mentioned gaps and presents a model that can determine an optimal retrofit plan for the whole building, considering both the envelope system and indoor systems, aiming at maximizing energy savings in the most cost-effective way and achieving a good rating from the EPC rating system to comply with the green building policy in South Africa. As reaching the best energy level from the EPC rating system for a building usually requires a high amount of investment, resulting in a long payback period, which is not attractive for decision makers in view of the vulnerable economic situation of South Africa, the proposed model treats the retrofit plan as a multi-year project, improving efficiency targets in consecutive years. That is to say, the model breaks down the once-off long-term project into smaller projects over multiple financial years with shorter payback periods. In that way, the financial concerns of the investors are alleviated. In addition, a tax incentive program to encourage energy saving investments in South Africa is considered in the optimization problem to explore the economic benefits of the retrofit projects fully. Considering both the envelope system and indoor systems, many systems and items that can be retrofitted and massive retrofit options available for them result in a large number of discrete decision variables for the optimization problem. The inherent non-linearity and multi-objective nature of the optimization problem and other factors such as the requirements of the EPC system make it difficult to solve the building retrofit problem. The complexity of the problem is further increased when the target buildings have many floors. In addition, there is a large number of parameters that need to be obtained in the building retrofit optimization problem. This requires a detailed energy audit of the buildings to be retrofitted, which is an expensive bottom-up modeling exercise. To address these challenges, two simplified methods to reduce the complexity of finding the optimal whole-building retrofit plans are proposed in Chapter 4. Lastly, an optimal maintenance planning strategy is presented in Chapter 5 to ensure the sustainability of the retrofit. It is natural that the performance of all the retrofitted items will degrade over time and consequently the energy savings achieved by the retrofit will diminish. The maintenance plan is therefore studied to restore the energy performance of the buildings after retrofit in a cost-effective way. Maintenance planning for the indoor systems is not considered in this study because it has been thoroughly investigated in the literature. In addition, a maintenance plan for the PV system involved in the retrofit of this study is investigated in Chapter 2. / Thesis (PhD)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

<p>Commercial buildings not only have significant impacts on occupants’ well-being, but also contribute to more than 19% of the total energy consumption in the United States. Along with improvements in building equipment efficiency and utilization of renewable energy, there has been significant focus on the development of advanced heating, ventilation, and air conditioning (HVAC) system controllers that incorporate predictions (e.g., occupancy patterns, weather forecasts) and current state information to execute optimization-based strategies. For example, model predictive control (MPC) provides a systematic implementation option using a system model and an optimization algorithm to adjust the control setpoints dynamically. This approach automatically satisfies component and operation constraints related to building dynamics, HVAC equipment, etc. However, the wide adaptation of advanced controls still faces several practical challenges: such approaches involve significant engineering effort and require site-specific solutions for complex problems that need to consider uncertain weather forecast and engaging the building occupants. This thesis explores smart building operation strategies to resolve such issues from the following three aspects. </p> <p>First, the thesis explores a stochastic model predictive control (SMPC) method for the optimal utilization of solar energy in buildings with integrated solar systems. This approach considers the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process, and achieves good solution quality. We use an emulator to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate considered, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.</p> <p>Second, this thesis explores user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the MPC strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the occupants’ overrides can contribute up to 55% of the HVAC energy consumption on average with MPC. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios. </p> <p>Finally, this thesis presents an approach based on meta-reinforcement learning (Meta-RL) that enables autonomous optimal building controls with minimum engineering effort. In reinforcement learning (RL), the controller acts as an agent that executes control actions in response to the real-time building system status and exogenous disturbances according to a policy. The agent has the ability to update the policy towards improving the energy efficiency and occupant satisfaction based on the previously achieved control performance. In order to ensure satisfactory performance upon deployment to a target building, the agent is trained using the Meta-RL algorithm beforehand with a model universe obtained from available building information, which is a probability measure over the possible building dynamical models. Starting from what is learned in the training process, the agent then fine-tunes the policy to adapt to the target building based on-site observations. The control performance and adaptability of the Meta-RL agent is evaluated using an emulator of a private office space over 3 summer months. For the system and climate under consideration, the Meta-RL agent can successfully maintain the indoor air temperature within the first week, and result in only 16% higher energy consumption in the 3^rd month than MPC, which serves as the theoretical upper performance bound. It also significantly outperforms the agents trained with conventional RL approach. </p>

In September 2020, Northeast China suffered three unprecedented typhoons in half a month, and there was freezing rain in early November, all of which led to the large-scale urban power failure. The occurrence of these phenomena makes people directly see climate change and its impact on the living environment of human beings. Many studies have shown that the cause of climate change is the increase of artificial greenhouse gas emissions since industrialization. In addition to the increase of extreme weather disasters, the most direct manifestations of climate change are the rising temperature, droughts, and rising sea levels. The building sector accounts for 39% of global greenhouse gas emissions and 36% of energy consumption. To ensure the long-term integrity and normal operation of buildings, we need to understand the impact of climate on buildings, and how to deal with it. This paper reviews the literature on climate change and building energy by searching search engines and literature databases. For extreme weather, most literature talks about the impact of power failure, the main strategy is to improve reliability, resilience, sustainability, and robustness, it can help reduce losses and recover as soon as possible. On the other hand, the methods of adaptation to and mitigation of non-disaster weather are reviewed from the perspective of sustainability. This paper mainly reviews the methods of passive technology and strategy for exemplary buildings, building envelope, passive ventilation, lighting/shading, solar energy, bioenergy, dehumidification, passive cooling, and design strategy. According to the local climate, the geographical characteristics of the building, to develop comprehensive passive technology and strategy, can meet or close to meet their energy saving, emission reduction, comfort needs. This paper can provide a technical and strategic reference for the building sector to deal with climate change. / <p></p><p>Via online ZOOM meeting Presentation</p><p></p><p></p><p></p><p></p><p></p><p></p>

The building sector has become the largest consumer of end use energy in the world, exceeding both the industry and the transportation sectors. Extensive types of energy saving techniques have been developed in the past two decades to mitigate the impact of buildings on the environment. Instead of the conventional active building environmental control approaches that solely rely on the mechanical air conditioning systems, increasing attention is given to the passive and mixed-mode approaches in buildings. This thesis aims to explore the integration of passive cooling approaches and active air conditioning approaches with different dehumidification features, by making effective use of the information on: 1) various dynamic response properties of the building system and mechanical plants, 2) diverse variations of the building boundary conditions over the whole operation process, 3) coupling effect and synergistic influence of the key operational parameters, and 4) numerous parameter conflicts in the integrated active-passive operation. These issues make the proposed integration a complex multifaceted process operation problem. In order to deal with these challenges, a systematic approach is developed by integrating a number of advanced building/system physical models and implementing well established advanced dynamic optimization algorithms. Firstly, a reduced-order model development and calibration framework is presented to generate differential-algebraic equations (DAE) based physical building models, by coupling with the high-order building energy simulations (i.e., EnergyPlus) and implementing MLE+ co-simulation programs in the Matlab platform. The reduced-order building model can describe the dynamic building thermal behaviors and address substantial time delay effects intrinsic in the building heat transfer and moisture migration. A calibration procedure is developed to balance the modelling complexity and the simulation accuracy. By making use of the advanced modeling and simulation features of EnergyPlus, the developed computational platform is able to handle real buildings with various geometric configurations, and offers the potential to cooperate with the dominant commercial building modeling software existing in the current AEC industry. Secondly, the physical model for the active air conditioning systems is developed, which is the other critical part for the dynamic optimization. By introducing and integrating a number of sub-models developed for specific building components, the model is able to specify the dynamic hygrothermal behavior and energy performance of the system under various operating conditions. Two representative air conditioning systems are investigated as the study cases: variable air volume systems (VAV) with mechanical dehumidification, and the desiccant wheel system (DW) with chemical dehumidification. The control variables and constraints representing the system operational characteristics are specified for the dynamic optimization. Thirdly, the integrated active-passive operations are formulated as dynamic optimization problems based on the above building and system physical models. The simultaneous collocation method is used in the solution algorithm to discretize the state and control variables, translating the optimization formulation into a nonlinear program (NLP). After collocation, the translated NLP problems for the daily integrated VAV/DW operation for a case zone have 1605/2181 variables, 1485/2037 equality constraints and 280/248 inequality constraints, respectively. It is found that IPOPT is able to provide the optimal solution within minutes using an 8-core 64-bit desktop, which illustrates the efficiency of the problem formulation. The case study results indicate that the approach can effectively improve the energy performance of the integrated active-passive operations, while maintaining acceptable indoor thermal comfort. Compared to the conventional local control strategies, the optimized strategies lead to remarkable energy saving percentages in different climate conditions: 29.77~48.76% for VAV and 27.85~41.33% for DW. The energy saving is contributed by the improvement of both the passive strategies (around 33%) and active strategies (around 67%). It is found that the thermal comfort constraint defined in the optimization also affects the energy saving. The total optimal energy consumption drops by around 3% if the value of the predicted percentage dissatisfied (PPD) limit is increased by one unit between 5~15%. It is also found that the fitted periodic weather data can lead to similar operation strategies in the dynamic optimization as the realistic data, and therefore can be a reasonable alternative when the more detailed realistic weather data is not available. The method described in the thesis can be generalized to supervise the operation design of building systems with different configurations.

While global efforts are made to reduce the emission of greenhouse gases and move towards a more sustainable society, the global energy demand is continuing to increase. Building energy consumption represents 20-40% of the world's total energy use, and Heating, Ventilation, and Air Conditioning (HVAC) answer for around 50% of this amount. Only a small share of the European Union's building stock is considered to be energy efficient, and many of these buildings will continue to operate until the year 2050 and on-wards. The main objective of this thesis was to benchmark the economic and environmental implications of increasing building heating efficiency. To answer the framed research questions, an experimental study was carried out. In the study, a machine learning based solution was constructed and then implemented in a multi-tenant building for 24 days. Using an Artificial Neural Network a new heating curve was predicted, based on historical data from the building. The post-experimental data was then analyzed using STATA as statistical software tool. The results show that the new heating curve was able to reduce the heating system supply temperature by 1.9°C, with a decrease in average indoor temperature of 0.097°C. The decrease in supply temperature resulted in a reduction of energy expenditure by approximately 10%. Using the new building specific heating curve, yearly cost reductions of almost 11,700SEK could be achieved. Furthermore, the increased efficiency was able to reduce CO2 emissions by 127,5kg yearly. This results helps shed light on the general weaknesses in building heating systems out there today, and shows that there is great potential of reducing building energy consumption in cost effective ways. Although the implemented solution might not be generally applicable for all building owners out there, it should act as an eye opener for building owners and help motivate them into assessing their building operation and start looking into new technologies. Moreover, the study provides legible incentives for both building owners and the society to further work together towards a more efficient and sustainable society.

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