<p>The main objective of this thesis is to investigate the energetic, economic and environmental impact of utilizing a novel Run-Around Membrane Energy Exchanger (RAMEE) in building HVAC systems. The RAMEE is an energy recovery ventilator that transfers heat and moisture between the exhaust air and the fresh outdoor ventilation air to reduce the energy required to condition the ventilation air. The RAMEE consists of two exchangers made of water vapor permeable membranes coupled with an aqueous salt solution.</p> <p>In order to examine the energy savings with the RAMEE, two different buildings (an office building and a health-care facility) were simulated using TRNSYS computer program in four different climatic conditions, i.e., cold-dry, cool-humid, hot-humid and hot-dry represented by Saskatoon, Chicago, Miami and Phoenix, respectively. It was found that the RAMEE significantly reduces the heating energy consumption in cold climates (Saskatoon and Chicago), especially in the hospital where the required ventilation rate is much higher than in the office building. On the other hand, the results showed that the RAMEE must be carefully controlled in summer to minimize the cooling energy consumption.</p> <p>The application of the RAMEE in an office building reduces the annual heating energy by 30% to 40% in cold climates (Saskatoon and Chicago) and the annual cooling energy by 8% to 15% in hot climates (Miami and Phoenix). It also reduces the size of heating equipment by 25% in cold climates, and the size of cooling equipment by 5% to 10% in hot climates. The payback period of the RAMEE depends on the air pressure drop across the exchangers. For a practical pressure drop of 2 cm of water across each exchanger, the payback of the RAMEE is 2 years in cold climates and 4 to 5 years in hot climates. The total annual energy saved with the RAMEE (including heating, cooling and fan energy) is found to be 30%, 28%, 5% and 10% in Saskatoon, Chicago, Miami and Phoenix, respectively.</p> <p>In the hospital, the RAMEE reduces the annual heating energy by 58% to 66% in cold climates, and the annual cooling energy by 10% to 18% in hot climates. When a RAMEE is used, the heating system can be downsized by 45% in cold climates and the cooling system can be downsized by 25% in hot climates. For a practical range of air pressure drop across the exchangers, the payback of the RAMEE is immediate in cold climates and 1 to 3 years in hot climates. The payback period in the hospital is, on average, 2 years faster than in the office building). The total annual energy saved with RAMEE is found to be 48%, 45%, 8% and 17% in Saskatoon, Chicago, Miami and Phoenix, respectively. The emission of greenhouse gases (in terms of CO₂-equivalent) can be reduced by 25% in cold climates and 11% in hot climates due to the lower energy use when employing a RAMEE.</p>

Under the pressure of energy shortage, energy saving has become one of the most important topics. The world isseeking different ways to follow the sustainable development concept and to solve the energy shortage crisis.This thesis is based on the idea of improving energy efficiency in the building industry which is one of thebiggest energy consumption industries. The aim of this paper is to simulate a renovation of an existing oldbuilding in Sweden according to the concept of building a Swedish Passive House and to see how much energycould be saved after the renovation. The target building Taljstenen 7A-C was built in 1960 in Uppsala and itbelongs to the housing company Uppsalahem. The target building is facing extensive renovation due to its age.An energy consumption model of the present building was built by the software VIP-Energy after measurementsand calculations. Based on the model, three important improvements are made in a simulative renovation process.The three improvements are insulating building envelope, installing a new FTX ventilation system with highefficient heat recovery system and installing solar collectors for hot top water and space heating. The resultsshow a significant reduction of energy consumption of the renovated building compared to the original onewhich is from 516MWh per year decreased to 371MWh. Although the renovated building did not completelyfulfil the Passive House Standard in Sweden, it still has improved to be a low energy building. The purpose ofsaving energy can be achieved.

This study examines the use of building modeling and energy simulations in the design process  of  a  building.  The  take-off  point  is  the  notion  of  energy  simulations  being needed early and throughout the building design process, and that the lack of energy simulations may be explained by the fact that they are time consuming and therefore often too expensive. A greater interoperability between software tools used by relevant disciplines,  such  as  the  architect  and  the  energy  specialist,  would  create  smoother workflows, which would reduce this cost and open up for more frequent and iterative energy  simulation  processes.  The  study  is  an  assessment  of  the  modeling  tool  Revit and  the  simulation  tool  Virtual  Environment  and  whether  they  can  create  smoother workflows, and make leeway for a more frequent use of energy simulations throughout the  design  process.  It  also  investigates  the  limitations  of  what  can  be  examined  by simulations in Virtual Environment. This will hopefully help clarify the future role of energy  simulations  in  design  processes.  The  method  is  a  trial  by  error  approach  of testing the two software tools by building and simulating a model. The results of these tests  show  that  the  workflow  is  not  optimal  (and  therefore  time  consuming)  for frequent  and  iterative simulations  throughout the  design  process,  but  it  also  reveals some  great  possibilities  of  what  can  be  performed  with  these  two  powerful  tools  at hand.  Further  development  with  regards  on  platform  independency  of  the  building information  model,  including  seamless  exporting  and  importing,  seems  necessary  to strengthen the future role of energy simulations.

<p>The main objective of this thesis is to investigate the energetic, economic and environmental impact of utilizing a novel Run-Around Membrane Energy Exchanger (RAMEE) in building HVAC systems. The RAMEE is an energy recovery ventilator that transfers heat and moisture between the exhaust air and the fresh outdoor ventilation air to reduce the energy required to condition the ventilation air. The RAMEE consists of two exchangers made of water vapor permeable membranes coupled with an aqueous salt solution.</p> <p>In order to examine the energy savings with the RAMEE, two different buildings (an office building and a health-care facility) were simulated using TRNSYS computer program in four different climatic conditions, i.e., cold-dry, cool-humid, hot-humid and hot-dry represented by Saskatoon, Chicago, Miami and Phoenix, respectively. It was found that the RAMEE significantly reduces the heating energy consumption in cold climates (Saskatoon and Chicago), especially in the hospital where the required ventilation rate is much higher than in the office building. On the other hand, the results showed that the RAMEE must be carefully controlled in summer to minimize the cooling energy consumption.</p> <p>The application of the RAMEE in an office building reduces the annual heating energy by 30% to 40% in cold climates (Saskatoon and Chicago) and the annual cooling energy by 8% to 15% in hot climates (Miami and Phoenix). It also reduces the size of heating equipment by 25% in cold climates, and the size of cooling equipment by 5% to 10% in hot climates. The payback period of the RAMEE depends on the air pressure drop across the exchangers. For a practical pressure drop of 2 cm of water across each exchanger, the payback of the RAMEE is 2 years in cold climates and 4 to 5 years in hot climates. The total annual energy saved with the RAMEE (including heating, cooling and fan energy) is found to be 30%, 28%, 5% and 10% in Saskatoon, Chicago, Miami and Phoenix, respectively.</p> <p>In the hospital, the RAMEE reduces the annual heating energy by 58% to 66% in cold climates, and the annual cooling energy by 10% to 18% in hot climates. When a RAMEE is used, the heating system can be downsized by 45% in cold climates and the cooling system can be downsized by 25% in hot climates. For a practical range of air pressure drop across the exchangers, the payback of the RAMEE is immediate in cold climates and 1 to 3 years in hot climates. The payback period in the hospital is, on average, 2 years faster than in the office building). The total annual energy saved with RAMEE is found to be 48%, 45%, 8% and 17% in Saskatoon, Chicago, Miami and Phoenix, respectively. The emission of greenhouse gases (in terms of CO₂-equivalent) can be reduced by 25% in cold climates and 11% in hot climates due to the lower energy use when employing a RAMEE.</p>

Increasing studies imply that predicted energy performance of buildings significantly deviates from actual measured energy use. This so-called "performance gap" may undermine one's confidence in energy-efficient buildings, and thereby the role of building energy efficiency in the national carbon reduction plan. Closing the performance gap becomes a daunting challenge for the involved professions, stimulating them to reflect on how to investigate and better understand the size, origins, and extent of the gap. The energy performance gap underlines the lack of prediction capability of current building energy models. Specifically, existing predictions are predominantly deterministic, providing point estimation over the future quantity or event of interest. It, thus, largely ignores the error and noise inherent in an uncertain future of building energy consumption. To overcome this, the thesis turns to a thriving area in engineering statistics that focuses on computation-based uncertainty quantification. The work provides theories and models that enable probabilistic prediction over future energy consumption, forming the basis of risk assessment in decision-making. Uncertainties that affect the wide variety of interacting systems in buildings are organized into five scales (meteorology - urban - building - systems - occupants). At each level both model form and input parameter uncertainty are characterized with probability, involving statistical modeling and parameter distributional analysis. The quantification of uncertainty at different system scales is accomplished using the network of collaborators established through an NSF-funded research project. The bottom-up uncertainty quantification approach, which deals with meta uncertainty, is fundamental for generic application of uncertainty analysis across different types of buildings, under different urban climate conditions, and in different usage scenarios. Probabilistic predictions are evaluated by two criteria: coverage and sharpness. The goal of probabilistic prediction is to maximize the sharpness of the predictive distributions subject to the coverage of the realized values. The method is evaluated on a set of buildings on the Georgia Tech campus. The energy consumption of each building is monitored in most cases by a collection of hourly sub-metered consumption data. This research shows that a good match of probabilistic predictions and the real building energy consumption in operation is achievable. Results from the six case buildings show that using the best point estimations of the probabilistic predictions reduces the mean absolute error (MAE) from 44% to 15% and the root mean squared error (RMSE) from 49% to 18% in total annual cooling energy consumption. As for monthly cooling energy consumption, the MAE decreases from 44% to 21% and the RMSE decreases from 53% to 28%. More importantly, the entire probability distributions are statistically verified at annual level of building energy predictions. Based on uncertainty and sensitivity analysis applied to these buildings, the thesis concludes that the proposed method significantly reduces the magnitude and effectively infers the origins of the building energy performance gap.

Within the design community, a growing number of researchers have shown interest in extending the value context to include design, such that designers focus on maximizing the 'value' of the product or service, rather than simply satisfying a set of requirements. Thus, by applying a value-driven approach to design, the design community hopes to show that the magnitude of cost and schedule overruns may be reduced, or even eliminated. However, a common criticism of value-driven approaches is that they are difficult to implement, and not sufficiently pragmatic to be used for large scale engineering problems. Further, some argue that less rigorous methods appear to provide reasonable results in practice, and so rigor is not necessary. To reconcile these disparate viewpoints, it must be shown that value-driven approaches contribute to the design process, and can be implemented in practice at a reasonable cost. In response, I propose that the cause for the lack of practicality in value-driven approaches is attributable to the lack of well established and verified methods and tools. This dissertation presents research that attempts to address this deficiency by first developing a better understanding of effectiveness for methods that seek to enable value-driven design. This investigation leads to a concise set of desired characteristics for methods for guiding the development of value-models which then motivate the creation of a Systematic Method for Developing Value Models (SMDVM). To evaluate the SMDVM, it is applied to the design and retrofit of buildings for energy efficiency. A simulation workbench is developed as a tool to automate the development and analysis of value models for building design and retrofit contexts. The workbench enables architects, engineers, and other practitioners to easily incorporate uncertainty into analyses of building energy consumption, as part of a value-driven approach to design and retrofit.

Residential space and water heating accounts for around 13% of the greenhouse gas emissions of the UK. Reducing this is essential for meeting the national emission reduction target of 80% by 2050 from the 1990 baseline. One of the strategies adopted for achieving this is focused around large scale shift towards electrical heating. This could lead to unsustainable disparity between the daily peak and off-peak electricity loads, large seasonal variation in electricity demands, and challenges of matching the short and long term supply with the demands. These challenges could impact the security and resilience of UK electricity supply, and needs to be addressed. Rechargeable Thermal Energy Storage (TES) in residential buildings can help overcome these challenges by enabling Heat Demand Shifts (HDS) to off-peak times, reducing the magnitude of the peak loads, and the difference between the peak and off-peak loads. To be effective a wide scale uptake of TES would be needed. For this to happen, the benefits and impacts of TES both for the demand side and the supply side have to be explored, which could vary considerably given the diverse physical, thermal, operational and occupancy characteristics of the UK housing stock. A greater understanding of the potential consequence of TES in buildings is necessary. Such knowledge could enable appropriate policy development to help drive the uptake of TES or to encourage development of alternative solutions. Through dynamic building simulation in TRNSYS, this work generated predictions of the space and water heating energy and power demands, and indoor temperature characteristics of the UK housing stock. Twelve building archetypes were created consisting of: Detached, semi-detached, mid-terrace and flat built forms with thermal insulation corresponding to the 1990 building regulation, and occupied floor areas of 70m2, 90m2 and 150m2. Typical occupancy and operational conditions were used to create twelve Base Case scenarios, and simulations performed for 60 winter days from 2nd January. HDS of 2, 3 and 4 hours from the grid peak time of 17:00 were simulated with sensible TES system sizes of 0.25m3, 0.5m3 and 0.75m3, and water storage temperatures of 75°C and 95°C. Parametric analysis were performed to determine the impacts and benefits of: thermal insulation equivalent to 1980, 1990 (Base Case), 2002 and 2010 building regulation; locations of Gatwick (Base Case) and Aberdeen; heating durations of 6, 9 (Base Case), 12 and 16 hours per day; thermostat settings of 19°C, 21°C (Base Case) and 23°C, and number of occupiers of 1 person and 3 persons (Base Case) per household. Good correlation was observed between the simulated results and published heat energy consumption data for buildings with similar thermal, physical, occupancy and operational conditions. The results allowed occupied space temperatures and overall daily and grid peak time energy consumption to be predicted for the range of building archetypes and parameter values considered, and the TES size necessary for a desired HDS to be determined. The main conclusions drawn include: The overall daily energy consumption predictions varied from 36.8kWh to 159.7kWh. During the critical grid peak time (17:00 to 21:00) the heat consumption varied from 4.2kWh to 58.7kWh, indicating the range of energy demands which could be shifted to off-peak times. On average, semi-detached, mid-terrace, and flat built forms consumed 7.0%, 13.8% and 22.7% less energy for space heating than the detached built form respectively. Thermal insulation changing from the 1990 building regulation level to the 1980 and 2010 building regulation levels could change the mean energy use by +14.7% and -19.6% respectively. A 0.25m3 TES size with 75°C water storage temperature could enable a 2 hour HDS, shifting 4.3kWh to 11.7kWh (mean 8.7kWh) to off peak times, in all 70m2 Base Case archetypes with the 60 day mean thermal comfort of 100%, but with the minimum space temperature occasionally dropping below an 18°C thermal comfort limit. A 0.5m3 TES size and water storage of 95°C could allow a 3 hour HDS, shifting 9.8kWh to 28.2kWh (mean 18.7kWh) to off peak times, in all 90m2 Base Case archetypes without thermal comfort degradation below 18°C. A 0.75m3 TES with a 95°C water temperature could provide 4 hour HDS, shifting 13.9kWh to 47.7kWh (mean 27.2kWh) to off peak times, in all 150m2 Base Case archetypes with 100% mean thermal comfort but with the 60 day minimum temperature occasionally dropping below the 18°C thermal comfort limit in the detached built form. Improving the thermal insulation of the buildings was found to be the best way to improve the effectiveness of HDS with TES, in terms of the demand shift period achievable with minimal thermal comfort impact. A 4 hour HDS with 100% thermal comfort is possible in all 90m2 floor area buildings with a 0.25m3 tank and a water storage temperature of 75°C provided that the thermal insulation is as per 2010 building regulation. Recommendations for further research include: 1) creating larger number of archetype models to reflect the housing stock; 2) using heat pumps as the heat source so that the mean effect on the grid from electric heating loads can be predicted; 3) taking into account the costs associated with taking up HDS with TES, in terms of capital expenses and space requirement for housing the TES system; 4) considering alternative methods of heat storage such as latent heat storage to enhance the storage capacity per unit volume; and 5) incorporating zonal temperature control, for example, only heating rooms that are occupied during the demand shift period, which could ensure better thermal comfort in the occupied space and extend the demand shift period.

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.

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.

In 2013, Stockholms Hamnar began a development project for Värtahamnen, one of Stockholms most important harbors, and also decided to build a new ferry terminal that is better suited to meet the increasing capacity demand. The new terminal will feature a borehole storage that will be used to cover the building’s heating and cooling demands. The boreholes have already been drilled and currently the construction of the building is being planned. The overall objective of this project is to study the new terminal and its borehole storage regarding certain input parameters (such as internal heat gains and the U-value of windows) that affect the building’s annual heating and cooling demands, as well as long-term temperature of the borehole storage. To do this, two modeling programs are used: IDA ICE and EED (Earth Energy Designer). The project focuses on three main parts. Part one is a sensitivity analysis of internal loads and construction specific parameters that shows how a variation in these affects the heating and cooling demands. To accomplish this, several models are created and simulated in IDA ICE. In part two, the long-term ground temperature is studied for two of the models analyzed in part one. This is done in both IDA (through a new borehole module) and EED, followed by a comparison of these results. The last part presents the possible amount of free cooling that can be taken from the ground. This estimation is made through simulations in EED, using altered load profiles of the two previously mentioned models. Additionally, this part covers the effects of a changed borehole configuration (number of boreholes, depth, layout, etc.). The results of the first part (the sensitivity analysis) show that there is a rather large variation in annual heating and cooling demands depending on what approach is used for estimating a reasonable amount of internal loads. One way to do this is to first determine the maximum possible load in each zone and then, when simulating the annual energy demand, reduce the total load in the whole building by a certain factor. Another approach is to, from the start of the building modeling, more accurately try to estimate the average amount of internal loads in each zone. In the second part, due to unbalanced load profiles for both analyzed models, the temperature of the borehole storage will increase over time if there is no limitation of the amount of cooling taken from the ground. The results of IDA generally agree with those of EED. In the last part of the project it is shown that a thermally more favorable borehole installation could increase the relative amount of free cooling from the ground, compared to the current installation.