The study focuses on the sensitivity and uncertainty analysis of occupancy-related parameters using Energyplus modeling method. The model is based on a real building Zero Energy Lab in Discovery Park, at University of North Texas. Four categories of parameters are analyzed: heating/cooling setpoint, lighting, equipment and occupancy. Influence coefficient (IC) is applied in the sensitivity study, in order to compare the impact of individual parameter on the overall building energy consumption. The study is conducted under Texas weather file as well as North Dakota weather file in order to find weather’s influence of sensitivity. Probabilistic collocation method (PCM) is utilized for uncertainty analysis, with an aim of predicting future energy consumption based on history or reference data set. From the study, it is found that cooling setpoint has the largest influence on overall energy consumption in both Texas and North Dakota, and occupancy number has the least influence. The analysis also indicates schedule’s influence on energy consumption. PCM is able to accurately predict future energy consumption with limited calculation, and has great advantage over Monte Carlo Method. The polynomial equations are generated in both 3-order and 6-order, and the 6-order equation is proved to have a better result, which is around 0.1% compared with real value.

This record of study summarizes the work accomplished during the internship at the Energy Systems Laboratory of the Texas Engineering Experiment Station. The internship project was to develop a tool to optimize the building parameters so that the overall building energy cost is minimized. A metaheuristic: genetic algorithm was identified as the solution algorithm and was implemented in the problem under study. Through two case studies, the impacts of the three genetic algorithm parameters, namely population size, crossover and mutation rates, on the algorithm's overall performance are also studied through statistical tests. Through these statistical tests, the optimum combination of above the mentioned parameters is also identified and applied. Finally, a performance analysis based on the case studies show that the tool achieved satisfactory results.

This research investigates the effects of daylighting in an existing elementary school in College Station, Texas. The conclusions are generalizable to similar school designs in hot and humid climates. This study focuses on the trends observed in the building??s heating, cooling, and lighting energy consumption due to daylighting, and the overall effect on total energy consumption. Skylights with 1% to 10% glazing surface to floor area and clerestories from 2 ft to 8 ft glazing height were analyzed to formulate balanced daylighting designs that could provide for decreased electricity and gas energy consumption and increased daylight illuminance levels and energy cost savings. Classroom and Library areas inside the case study school building were analyzed using walk-throughs and daylight factor measurements to understand existing lighting conditions and the potential for daylighting. Physical scale models of the study spaces with and without daylighting alternatives were built for daylight factor and daylight penetration analysis. Computer simulation models were created for the base case and all proposed daylighting designs for building energy performance evaluation using the DOE-2 building energy simulation program. Daylight factors from the actual spaces, physical model measurements, and computer simulation outputs were studied for trendsin interior daylight illuminance levels. Annual energy consumption analyses were performed using DOE-2 and involved heating, cooling, and electrical energy use comparisons of all proposed designs with the base case. One design each from the skylight and clerestory cases, and an overall design based upon the performance criteria are proposed for the existing school building. The building energy analyses suggested that a considerable reduction in artificial lighting and total electricity use could be achieved through proper sizing of skylights and clerestories. Heating energy use stayed almost constant in all cases. Considering all different trends in energy use, all the proposed cases perform better than the base case in terms of total energy savings. The spaces analyzed constituted 15% of total school area, and projected savings would be much higher if daylighting could be applied to the entire school building.

This record of study summarizes the work accomplished during the internship at the Energy Systems Laboratory of the Texas Engineering Experiment Station. The internship project was to develop a tool to optimize the building parameters so that the overall building energy cost is minimized. A metaheuristic: genetic algorithm was identified as the solution algorithm and was implemented in the problem under study. Through two case studies, the impacts of the three genetic algorithm parameters, namely population size, crossover and mutation rates, on the algorithm's overall performance are also studied through statistical tests. Through these statistical tests, the optimum combination of above the mentioned parameters is also identified and applied. Finally, a performance analysis based on the case studies show that the tool achieved satisfactory results.

Miljöbyggnad is a Swedish system for certifying building in regarding to energy, indoor climate and materials. Energy usage in built environment occupies more than a third of total energy consumption and greenhouse gas emissions in Sweden (SEA, 2008). Among fifteen indicators regulated by Miljöbyggnad, four indicators which consist of specific energy use, thermal climate winter, thermal climate summer and daylight have been analyzed in this report. There has two objectives for the project. The first objective is to make optimized approaches for the building according to baseline simulation model. And the second objective is to make assessment of the optimized model based on Miljöbyggnad environmental certification. As a conclusion, the implemented approaches helped to improve indoor thermal comfort and decrease demand of operational electricity for lighting. The four analyzed indicator of the optimized model have achieved GOLD level according to criteria regulated by Miljöbyggnad.

The building sector represents the largest energy consumer among the United States' end use sectors. As a result, the public and private sector will continue to place great emphasis on designing energy efficient buildings that minimize operating costs while maintaining a healthy environment for its occupants. Creating design-phase building energy models can facilitate the process of selecting life-cycle appropriate design strategies aimed at maximizing building energy efficiency. The primary objective of this research study is to gain greater insight into likely causes of variation between energy predictions derived from building energy models and building energy performance during post-occupancy. Identifying sources of error can be used to improve future modeling efforts that can potentially lead to greater accuracy and better decisions made during the building's design phase. My research approach is to develop a method for conducting retrospective analysis of building energy models in the areas that affect the building's predicted and actual energy consumption. This entails collecting pre-construction and post-occupancy related data from various entities that exhibit influence on the building's energy performance. The method is then applied to recently-constructed military dormitory buildings that utilized building energy modeling and now have actual, metered building energy consumption data. The study also examines how building occupancy impacts energy performance. The value of this work will provide additional insight to future building energy modeling efforts. / text

In 2010 the housing stock was responsible for 30.5% of all energy consumed in the UK. The UK government has set a transition target to reduce the energy used from space heating in dwellings by 29% by 2020 as part of their drive to lower CO2 emissions and mitigate the risks of global climate change. Housing stock energy models have been developed as research tools to identify pathways to a low energy future. These tools use assumptions about how homes are heated that may reduce their effectiveness at making accurate energy predictions. This thesis describes the collection and analysis of temperature data from over 300 homes in Leicester to develop better understanding of how dwellings are heated. The temperature measurements were assessed for error and a final sample of 249 dwellings was established. Mean winter temperatures (December February) were found to be 18.5°C and 17.4°C for living rooms and bedrooms which are comparable with temperatures reported in previous studies. Statistically significant relationships were established between seven descriptors; three technical (house type, house age and wall type) and four social (household size, employment status, age of oldest occupants and tenure). Only 24% of the variation in mean winter temperature could be explained by these descriptors. Ten heating practice metrics were developed to give insight into how homes are heated; these included the duration of the heating period and the average temperature when heated. Statistically significant relationships were found between the heating practices and a number of technical and social household descriptors. It is concluded that the variation in heating practices which relates to social household descriptors will result in models being unable to make accurate predictions at the regional of city scale. Furthermore, this work has shown flaws in the idealised temperature profile as used in BREDEM. It is suggested that the findings of this work are considered in the development of future stock models.

Building sector takes a large part of Swedish domestic energy use. Swedish government had set goal that required energy consumption should decrease by 20% in year 2020 compared to 1995. Public house companies will play an important role in the process.  The work studies a typical Swedish Multi-family dwelling, built in 1960s and belonging to Älvkarlebyhus AB. These buildings were given enhanced air tightness in recent years which yielded a good result. This work focuses on improving the old ventilation system and decreasing energy consumption.  Building energy simulation tool IDA ICE was used to model the object building and to examine the effectiveness of the new system. The tested energy efficiency measures include upgraded ventilation system with heat exchanger, and the installation of demand control (DCV) to the ventilation. Both energy, environmental and economic aspects are considered in the study. The result showed the total energy demand decreased 35% with renovation. Total investment for all buildings correspond to 5 760 000 SEK. New system could save 237 872 SEK/year and payback time will be 24 years.

The potential of static pressure reset (SPR) control to save fan energy in variable air volume HVAC systems has been well documented. Current research has focused on the creation of reset strategies depending on specific system features. As the commissioning process has begun to require the prediction of savings, knowledge of the extent to which various SPR control strategies impact fan energy has become increasingly important. This research aims to document existing SPR control strategies and utilize building data and simulation to estimate fan energy use. A comprehensive review of the literature pertaining to SPR control was performed and the results were organized into a top-down flow chart tool. Based on the type of feedback available from a particular system, or lack thereof, this tool will facilitate the selection of a SPR control strategy. A field experiment was conducted on a single duct variable air volume system with fixed discharge air temperature and static pressure setpoints. Finally, an air-side model of the experimental system was created using detailed building design information and calibrated using field measurements. This model was used to estimate the fan energy required to supply the trended airflow data using fixed static pressure (FSP) and SPR control based on zone demand, system demand, and outside air temperature. While utilizing trend data from November 1, 2008 to February 12, 2009, the FSP control of the experimental system was used as the baseline for ranking the energy savings potential of nine different forms of duct static pressure control. The highest savings (73-74%) were achieved using zonal demand based SPR control. System demand based SPR control yielded savings ranging from 59 to 76%, which increased when the duct sensor was positioned near the fan discharge and under similar zone load conditions. The outside air temperature based SPR control yielded savings of 65% since the experimental system supplied primarily perimeter zones. Finally, increasing the FSP setpoint from 2 to 3 inWG increased fan energy by 45%, while decreasing the setpoint from 2 to 1 inWG decreased fan energy by 41%.

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