While more efficient use of energy is increasingly vital to the development of the modern industrialized world, emerging visualization tools and approaches of telling data stories provide an opportunity for the exploration of a wide range of topics related to energy consumption and conservation (Olsen, 2017). Telling energy stories using data visualization has generated great interest among journalists, designers and scientific researchers; over time it has been proven to be effective to provide knowledge and insights (Holmes, 2007). This thesis proposes a new angle of tackling the challenge of designing visualization experience for building energy data, which aims to invite the users to think besides the established data narratives, augment the knowledge and insight of energy-related issues, and potentially trigger ecological responsible behaviors, by investigating and evaluating the efficacy of the existing interactive energy data visualization projects, and experimenting with user-centric interactive interface and unusual visual expressions though the development of a data visualization prototype.

Buildings are responsible for a significant portion of energy consumption worldwide. Intelligent buildings have been devised as a potential solution, where energy consumption and building use are harmonised. At the heart of the intelligent building is the building energy management system (BEMS), the central platform which manages and coordinates all the building monitoring and control subsystems, such as heating and lighting loads. There is often a disconnect between the BEMS and the building it is installed in, leading to inefficient operation, due to incongruous commissioning of sensors and control systems. In these cases, the BEMS has a lack of knowledge of the building form and function, requiring further complex optimisation, to facilitate efficient all year round operation. Flawed BEMS configurations can then lead to ‘sick buildings’. Recently, building energy performance simulation (BEPS) has been viewed as a conceptual solution to assist in efficient building control. Building energy simulation models offer a virtual environment to test many scenarios of BEMS operation strategies and the ability to quickly evaluate their effects on energy consumption and occupant comfort. Challenges include having an accurate building model, but recent advances in building information modelling (BIM) offer the chance to leverage existing building data, which can be translated into a form understood by the building simulator. This study will address these challenges, by developing and integrating a BEMS, with a BIM for BEPS assisted predictive control, and assessing the outcome and potential of the integration.

This research investigates the potential of a solar powered cooling system for single family houses in Pakistan. The system comprises water heating evacuated tube solar collectors, a hot water storage tank, and an absorption chiller. A literature review was carried out covering: • Energy situation, climate, and renewable energy potential in Pakistan; • Energy and thermal comfort in buildings, particularly for hot climates; • Solar collectors and solar cooling systems, particularly for hot climates; • Dynamic thermal simulation and weather data for solar energy systems and buildings. It was found that Pakistan is short of energy and that there is a great need to cool buildings. Renewable energy cooling systems are, therefore, of interest. The system described above was selected, as it was found that solar energy is abundant in Pakistan when cooling is required; thermal systems can be more economical than photovoltaics for hot climates and suitable components (collectors, absorption chillers, etc.) are commercially available. The TRNSYS dynamic thermal simulation program was selected as the main research tool, as it has been tested for solar energy and building applications by many researchers and suitable experimental facilities were not available. A simple typical building in Pakistan with a solar cooling system was simulated. Optimum values for key parameters were found by repeated simulations. It was concluded that the system would be able to provide cooling when required without an auxiliary heat source, and that an evacuated tube collector with a gross area of 12 m2, a collector flow rate of 165 kg/h, and a storage tank volume of 2 m3 would provide satisfactory performance for a 3.52 kW absorption chiller integrated with 42m3 single room. The results were in good agreement with published results from other researchers. Sensitivity analysis was carried out for the collector area, collector flow rate and storage tank size. It was found that varying the collector area had the largest effect on system performance, followed by varying the storage tank volume. Varying the collector flow rate had the smallest effect. It is recommended that solar cooling systems should be considered for Pakistan, and that further research should be carried out into reducing building cooling loads, using surplus heat for other loads, improving the performance of the proposed solar cooling system, and comparing it with other systems such as photovoltaics.

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.

The need for efficient, sustainable, and planned utilization of resources is ever more critical. In the U.S. alone, buildings consume 34.8 Quadrillion (1015) BTU of energy annually at a cost of $1.4 Trillion. Of this energy 58% is utilized for heating and air conditioning. Several building energy analysis tools have been developed to assess energy demands and lifecycle energy costs in buildings. Such analyses are also essential for an efficient HVAC design that overcomes the pitfalls of an under/over-designed system. DOE-2 is among the most widely known full building energy analysis models. It also constitutes the simulation engine of other prominent software such as eQUEST, EnergyPro, PowerDOE. Therefore, it is essential that DOE-2 energy simulations be characterized by high accuracy. Infiltration is an uncontrolled process through which outside air leaks into a building. Studies have estimated infiltration to account for up to 50% of a building’s energy demand. This, considered alongside the annual cost of buildings energy consumption, reveals the costs of air infiltration. It also stresses the need that prominent building energy simulation engines accurately account for its impact. In this research the relative accuracy of current air infiltration calculation methods is evaluated against an intricate Multiphysics Hygrothermal CFD building envelope analysis. The full-scale CFD analysis is based on a meticulous representation of cracking in building envelopes and on real-life conditions. The research found that even the most advanced current infiltration methods, including in DOE-2, are at up to 96.13% relative error versus CFD analysis. An Enhanced Model for Combined Heat and Air Infiltration Simulation was developed. The model resulted in 91.6% improvement in relative accuracy over current models. It reduces error versus CFD analysis to less than 4.5% while requiring less than 1% of the time required for such a complex hygrothermal analysis. The algorithm used in our model was demonstrated to be easy to integrate into DOE-2 and other engines as a standalone method for evaluating infiltration heat loads. This will vastly increase the accuracy of such simulation engines while maintaining their speed and ease of use characteristics that make them very widely used in building design.

There is interest concerning the energy performance of buildings in the United States. Buildings, whether residential, commercial or institutional, generally underperform in terms of energy efficiency when compared to buildings that are constructed following sustainably and energy efficiency standards. A substantial percentage of energy loss in these buildings is associated with the thermal efficiency of its envelope (exterior walls, windows roof, floors and doors). The objective of this study will evaluate the results of three energy modeling techniques developed to investigate the energy transfer through the envelope of existing campus buildings. The techniques employed are solving the heat transfer calculations using spreadsheets, using a stand-alone modeling software (OpenStudio) and using an integrated building energy modeling software (eQuest) employed in Autodesk Revit. The first technique is somewhat different from the other two because it does not require a 3D representation of the building to be generated as the first step in the modeling process. It is the application of a mathematical methodology employing heat transfer algorithms entered into the spreadsheet’s cells to estimate the heat transfer through the building envelope. Data needed for this technique are weather data of the buildings location, surface area of the building envelope, and the overall heat transfer coefficient (U-value) of each component of the building envelope. The OpenStudio technique involves a 3D representation of the building. The building is drawn on a 3D modeling computer program called SketchupPro, which communicates directly to the OpenStudio energy modelling interface. The building operations as well as the building characteristics, such as the composition and type of the elements that made up the building envelop, the thermal zone, occupancy schedule and the space type was inputted in the OpenStudio engine. The OpenStudio engine runs the simulation and generates a detail result about the energy usage and energy transfer in the building. The third method that employs AutoCAD Revit software is a standalone technique that does not require an external software for sketching the building model. Revit the ability to draw the model as well as perform the energy analysis at the same time with the aid of inbuilt eQuest modeling engine. The model in Revit is generated with the right building envelope characteristics as the existing building and the weather file. The process is somewhat similar to the OpenStudio technique; the main difference is the level of detail and limitation provided by both the energy modeling engine (eQuest and EnergyPlus). At the end of the simulation, the building energy modeling using Autodesk Revit presents a detailed result of the energy usage and energy flow in the building. The underlying reason of the comparison of three techniques is to understand the simplest, most efficient, accurate method to quantify heat transfer through the building envelope. By the end of this study, the most efficient technique for investigating the building envelope will be expected to be the EnergyPlus technique because of the usage simplicity, ability to take in a lot of details required for simulation and the periodical software updates.

Providing conditioned and fully controlled room is the final goal for having a comfortable building. But on the other hand making smart controllers to provide the required cooling or heating load depending on occupants' real time feeling is necessary. This study has emphasized on finding a meaningful and steady state parameter in human body that can be interpreted as comfort criterion which can be expressed as the general occupants' sensation through their ambient temperature. There are lots of researches on human physiological behavior in different situations and also different body parts reaction to the same ambient situation. Body parts which have the biggest reliable linear fluctuation to the changes are the best subject for this research. For these tests, wrist and palm have been selected and their temperatures on different people have been measured accurately with thermal camera to follow the temperature trend on various comfort levels. It is found that each person reaches to his own unique temperature on these two spots, when he/ she feels comfortable, or in other word each person's body temperature is a precise nominate for comfort feeling of that individual. So in future by having this unique comfort parameter and applying them to the HVAC system temperature control, controlling the dynamic temperature and correlating the indoor condition depending on the occupants instant thermal comfort level, would be a rational choice to bring convenience while energy has been saved more.

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>

Campus dormitories for the University of North Texas house over 5500 students per year; each one of them requires certain comfortable living conditions while they live there. There is an inherit amount of money required in order to achieve minimal comfort levels; the cost is mostly natural gas for water and room heating and electricity for cooling, lighting and peripherals. The US Department of Energy has developed several programs to aid in performing energy simulations to help those interested design more cost effective building designs. Energy-10 is such a program that allows users to conduct whole house evaluations by reviewing and altering a few parameters such as building materials, solar heating, energy efficient windows etc. The idea of this project was to recreate a campus dormitory and try to emulate existent energy consumption then try to find ways of lowering that usage while maintaining a high level of personal comfort.