By 2050, the world’s population is expected to reach 9.7 billion, with over half living in urban settlements (United Nations, 2015). Planning and designing new urban developments and improving existing infrastructure will create or reshape urban landscapes and will carry significant implications for energy consumption, infrastructure costs, and the urban microclimate on a larger scale. Researchers and industry professionals must recognize how changes in land use affect the urban microclimate and, therefore, building energy consumption. Built environment and microclimate studies commonly involve modeling or experimenting with mass and energy exchanges between natural and the built environment. Current methods to quantify these exchanges include the isolated use of microclimate and building energy simulation tools. However, current urban planning and building design processes lack a holistic and seamless approach to quantifying all thermodynamic interactions between natural and built environments; nor is there a method for communicating and visualizing the simulated building energy data. This dissertation has developed a coupling method to quantify the effects of the urban microclimate on building energy consumption. The coupling method was tested on a medium-sized office building and applied to a design case, a redevelopment project in Pittsburgh, PA. Three distinct approaches were used. First, to develop the coupling method, a study was conducted to quantify the importance of accurate microclimate model initialization for achieving simulation results that represent measured data. This initialization study was conducted for 24 cases in the Pittsburgh climate. The initialization study developed a rule-based method for estimating the number of ENVI-met simulations needed to predict the microclimate for an annual period. Second, a coupling method was developed to quantify these microclimate effects on building energy consumption. The Center for Sustainable Landscapes (CSL) building was used as a test-case for this coupling method to measure improvement in predicting building heating and cooling energy consumption. Results show that the coupling method, more than the TMY3 weather data used for energy simulations, can improve building energy consumption predictions for the winter and summer months. Third, to demonstrate industry implications, the coupling method was applied to a design case, the Lower Hill District Redevelopment, Pittsburgh, PA. Comparing the decoupled energy model and TMY3 weather data revealed a high degree of variation in the heating and cooling energy consumption. Overall results reinforced the hypothesis that building surface level coupling is not essential if the energy model accounts for the microclimate effects. A Design Decision Support (DDS) method was also developed as a tool for project stakeholders to communicate high-fidelity simulated energy data.

Globally, buildings utilize 35 % of the final energy use and contribute to approximately one-third of CO2 emissions. Hence, reducing the energy use of buildings contributes to a large amount of CO2 emissions to be decreased. The building’s energy use is affected by many parameters, including wind which plays an important role in building energy use. In this thesis, we aim to analyze the impact of wind parameters on building’s energy use on two multi-family building types with natural ventilation at various wind sheltering conditions at different climatic zones in Sweden. Building energy simulation models (BES) of a standalone and an attached building located in Visby, Sweden, were constructed with the use of the dynamic BES IDA ICE. Luleå and Malmö were taken as other two study locations to investigate the impact from different climate zones. The simulations were performed with the constructed calculation models, with the various wind sheltering conditions at the different climatic zones to calculate the energy use of the buildings and ventilation and infiltration losses. The sensitivity analysis was then carried out based on changing the wind profile of the climate file to evaluate the impact of wind on the ventilation and infiltration losses, as well as the heat energy use of the building. The results showed that the energy use for space heating of the attached building was 89 kWh/m2 (38 %) lower than the standalone building. The energy use varies between 9–20 kWh/m2 (3–10 %) considering the exposed, semi-exposed and sheltered wind condition for the two building types. In the different climate zones, Luleå has 47 kWh/m2 higher energy use compared to Visby and Malmö for the standalone building. The corresponding figure for the attached building is 25 kWh/m2. The sensitivity analysis show that when the wind speed is increased by 100 %, the ventilation and infiltration losses increase between 3563–18683 kWh (54–61 %) while the energy use of the building increases between 11–54 kWh/m2 (20–27 %).

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.

Buildings play a central role for livability and carbon footprint of urban areas. Ambitious energy saving and emission reduction targets created a need for a new generation of decisionsupport methods and tools that allow for detailed analysis of urban energy on a large scale. Urban building energy modeling (UBEM) that has emerged recently is an efficient approach to assess energy performance of multiple buildings and system effects from urban energy interventions. However, the further upscale of UBEMs is significantly limited due to the lack of automation for building energy performance (BEP) simulations required for such models in large amounts. This thesis aimed to explore challenges for automation of BEP simulations, and to develop a prototype tool that would serve as a middleware between UBEM and BEP simulation engine, focusing on the IDA ICE simulation software. The result of this thesis is icepy — a tool for automation of BEP simulations in IDA ICE. It uses IDA ICE API and Lisp scripting to provide interaction between UBEM process and IDA ICE in order to generate initial simulation model (IDM), execute simulation and manage results in an automated way. Being implemented as a Python package, it allows to modify multiple IDMs or export simulation results with a few lines of code. The developed tool has been tested and validated for the case building in Minneberg, Stockholm. The automation capabilities provided by icepy has allowed to perform sensitivity analysis for building design parameters as was demonstrated for the window-to-wall ratio (WWR) and three various algorithms for window distribution. The resulting tool has limited functionality as it addressed building envelopes which is only one component of building simulation. However, it has proved to be an efficient approach to automate simulation process and has shown a good potential for further development of such tools. / Byggnader spelar en central roll för urbana områdens levbarhet och koldioxidavtryck. Ambitiösa mål för energibesparing och utsläppsminskning har skapat ett behov av en ny generation beslutsstödmetoder och verktyg som möjliggör detaljerad analys av städers energianvändning i stor skala. Urban byggnadsenergimodellering (UBEM) har nyligen utvecklats och är ett effektivt tillvägagångssätt för att bedöma energiprestanda för flera byggnader och systemeffekter för olika energiåtgärder inom den urban miljön. Den ytterligare uppskalningen av UBEM är dock begränsad på grund av bristen på automation av simulering som är inriktade på byggnadsenergiprestanda (BEP), vilket krävs för att hantera stora byggnadsbestånd. Det här examensarbetet syftar till att utforska utmaningar med automatisering av BEP-simuleringar och att utveckla en prototyp som ska fungera som en mellanprogramvara mellan UBEM och BEP-simuleringsmotorer, med fokus på IDA ICE(som är en simuleringsprogramvara). Resultatet av examensarbetet är icepy, som är ett verktyg för att automatisera BEP-simuleringar i IDA-ICE. Icepy använder IDA ICE API och Lispskript för att tillhandahålla interaktion mellan UBEM-processen och IDA ICE för att generera en initial simuleringsmodell (IDM), utför själva simuleringen och slutligen hanterar resultatet på ett automatiserat sätt. Genom att icepy implementeras som ett Pythonpaket kan den modifiera flera IDM:er och även exportera simuleringsresultat med några få kodrader. Området Minneberg i Stockholm har använts i en fallstudie för att validera och testa verktyget. Automatiseringsfunktionerna i icepy har möjliggjort känslighetsanalyser för olika byggnadsdesignparametrar, exempelvis studerades påverkan av olika värden på förhållandet mellan fönster och väggar genom användning av tre olika algoritmer för fönsterdistributioner. Det utvecklade verktyget har begränsningar i funktionalitet framförallt på grund av att enbart byggnadens ytterskal studerades i byggnadsenergisimuleringarna. Verktyget har dock visat sig vara ett effektivt tillvägagångssätt för att automatisera simuleringsprocesser, vilket visar på en god potential att också vidareutveckla dessa verktyg.

The diploma thesis is to bring knowledge from the field of energy and simulation evaluation of buildings. Further, there is an introduction to the issue of energy and environmental assessment, legislative documents. A brief procedure for creating an energy model in a simulation program, then setting the model. Evaluated results from DesignBuilder that are in the form of charts. And also the evaluation of the measured data in the form of graphs from Libuše object in Karlova Studánka.

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.

Low-energy buildings have, in recent years, gained attention and moved towards a large-scale introduction in the residential sector. During this process, national and international criteria for energy use in buildings have become stricter and the European Union has through the Energy Performance of Buildings Directive imposed on member states to adapt their building regulations for ‘Nearly Zero Energy Buildings’, which by 2021 should be standard for new buildings. With a primary focus on new terraced and detached houses, this thesis analyses how the concept of low-energy buildings may be further developed to reduce the energy use in the residential sector. The main attention is on the technical performance in terms of indoor climate and heat consumption as well as on the market diffusion of low-energy buildings into the housing market. A multidisciplinary approach is applied, which here means that the concept of low-energy buildings is investigated from different perspectives as well as on different system levels. The thesis thus encompasses methods from both engineering and social sciences and approaches the studied areas through literature surveys, interviews, assessments and simulations. The thesis reveals how an increased process integration of the building’s energy system can improve the thermal comfort in low-energy buildings. Moreover, it makes use of learning algorithms – in this case artificial neural networks – to study how the heat consumption can be predicted in a low-energy building in the Swedish climate. The thesis further focuses on the low-energy building as an element in our society and it provides a market diffusion analysis to gain understanding of the contextualisation process. In addition, it suggests possible approaches to increase the market share of low-energy buildings. / <p>QC 20140321</p>

The focus of this study was to investigate an alternative and more cost effective solution for occupancy sensing in commercial office buildings. The intended purpose of this solution is to aid in efficient energy management. The main requirements were that the proposed solution made use of existing infrastructure only, and provided a means to focus on occupant location. This research was undertaken due to current solutions making use of custom occupancy sensors that are relatively costly and troublesome to implement. These solutions focus mainly on monitoring environmental changes, and not the physical locations of the occupants themselves. Furthermore, current occupancy sensing solutions are unable to provide proximity and timing information that indicate how far an occupant is located from a specific area, or how long the occupant resided there. The research question was answered by conducting a proof of concept study with data simulated in the OMNeT++ environment in conjunction with the MiXiM framework for wireless networks. The proposed solution investigated the fidelity of existing WiFi infrastructure for occupancy sensing, this entailed the creation of a Virtual Occupancy Sensor (VOS) that implemented RSS-based localisation for an occupant’s WiFi devices. Localisation was implemented with three different location estimation techniques; these were trilateration, constrained nearest neighbour RF mapping and unconstrained nearest neighbour RF mapping. The obtained positioning data was interpreted by a developed intelligent agent that was able to transform this regular position data into relevant occupancy information. This information included a distance from office measurement and an occupancy result that can be interpreted by existing energy management systems. The accuracy and operational behaviour of the developed VOS were tested with various scenarios. Sensitivity analysis and extreme condition testing were also conducted. Results showed that the constrained nearest neighbour RF mapping approach is the most accurate, and is best suited for occupancy determination. The created VOS system can function correctly with various tested sensitivities and device loads. Furthermore results indicated that the VOS is very accurate in determining room level occupancy although the accuracy of the position coordinate estimations fluctuated considerably. The operational behaviour of the VOS could be validated for all investigated scenarios. It was determined that the developed VOS can be deemed fit for its intended purpose, and is able to give indication to occupant proximity and movement timing. The conducted research confirmed the fidelity of WiFi infrastructure for occupancy sensing, and that the developed VOS can be considered a viable and cost effective alternative to current occupancy sensing solutions. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

The focus of this study was to investigate an alternative and more cost effective solution for occupancy sensing in commercial office buildings. The intended purpose of this solution is to aid in efficient energy management. The main requirements were that the proposed solution made use of existing infrastructure only, and provided a means to focus on occupant location. This research was undertaken due to current solutions making use of custom occupancy sensors that are relatively costly and troublesome to implement. These solutions focus mainly on monitoring environmental changes, and not the physical locations of the occupants themselves. Furthermore, current occupancy sensing solutions are unable to provide proximity and timing information that indicate how far an occupant is located from a specific area, or how long the occupant resided there. The research question was answered by conducting a proof of concept study with data simulated in the OMNeT++ environment in conjunction with the MiXiM framework for wireless networks. The proposed solution investigated the fidelity of existing WiFi infrastructure for occupancy sensing, this entailed the creation of a Virtual Occupancy Sensor (VOS) that implemented RSS-based localisation for an occupant’s WiFi devices. Localisation was implemented with three different location estimation techniques; these were trilateration, constrained nearest neighbour RF mapping and unconstrained nearest neighbour RF mapping. The obtained positioning data was interpreted by a developed intelligent agent that was able to transform this regular position data into relevant occupancy information. This information included a distance from office measurement and an occupancy result that can be interpreted by existing energy management systems. The accuracy and operational behaviour of the developed VOS were tested with various scenarios. Sensitivity analysis and extreme condition testing were also conducted. Results showed that the constrained nearest neighbour RF mapping approach is the most accurate, and is best suited for occupancy determination. The created VOS system can function correctly with various tested sensitivities and device loads. Furthermore results indicated that the VOS is very accurate in determining room level occupancy although the accuracy of the position coordinate estimations fluctuated considerably. The operational behaviour of the VOS could be validated for all investigated scenarios. It was determined that the developed VOS can be deemed fit for its intended purpose, and is able to give indication to occupant proximity and movement timing. The conducted research confirmed the fidelity of WiFi infrastructure for occupancy sensing, and that the developed VOS can be considered a viable and cost effective alternative to current occupancy sensing solutions. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

Increased energy performance of the building stock of European Union is seen as an important measure towards mitigating climate change, increasing resource utilisation efficiency and energy supply security. Whether to improve the supply-side, the demand-side or both is an open issue. This conflict is even more apparent in countries such as Sweden with a high penetration of district heating (DH). Many Swedish DH systems have high share of secondary energy resources such as forest industry residuals, waste material incineration and waste heat; and resource efficient cogeneration of electricity in combined heat and power (CHP) plants. When implementing an energy conservation measure (ECM) in a DH connected building stock, it will affect the operation of the whole DH system. If there are CHP plants and the cogeneration of electricity decreases due to an ECM, and this electricity is valued higher than the fuel savings, the consequences of the ECM would be negative.  These complex relationships are investigated by conducting a case study on the Eskilstuna DH system, a renewable energy supply system with relatively high share of cogenerated electricity. Heat demand profiles of ECMs are determined by building energy simulation, using recently deep energy retrofitted multifamily buildings of the “Million Programme”-era in Eskilstuna as model basis. How implementing ECMs impact on the DH system’s heat and electricity production under different electricity revenue scenarios has been computed and evaluated in terms of resource efficiency and CO2 emissions.  The results show that different ECMs in the buildings impact differently on the DH system. Measures such as improved insulation level of the building’s envelope, that decrease the heat demand’s dependence to outdoor temperature, increase the amount of cogenerated electricity. While measures such as thermal solar panels, which save heat during summer, affects the absolute amount of cogenerated electricity negatively. Revenues from cogenerated electricity influence the amount of cost-effectively produced electricity much more than the impact from ECMs. Environmental benefits of the ECMs, measured in CO2 emissions and primary energy consumption, are quite small in DH systems that have high share of forest residual fuels and electricity cogeneration. The consequences can even be negative if ECMs lead to increased need of imported electricity that is produced resource inefficiently or/and by fossil fuels. However, all studied ECMs increase the relative amount of cogenerated electricity, the ratio between amount of cogenerated electricity and the heat load. This implied that all ECMs increase the overall efficiency of the Eskilstuna DH system.