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

Accurate modeling of energy flows in buildings is necessary for optimization of mechanical systems, and architectural designs and components. One specific process which has been studied little is that of forced convection on the interior surfaces of window assemblies, which is present in the majority of newly constructed commercial buildings. To this end, energy flows associated with a specific Heating Ventilation and Air-Conditioning (HVAC) configuration- a floor register near a glass curtain wall with or without Venetian blinds- are analyzed experimentally and partially described with accepted theory. Natural convection at the same surface is analyzed as well, both to establish a baseline and to experimentally validate the experimental setup. A 60 cubic meter environmental chamber with precisely controlled interior conditions and electrical resistance heating panels is employed to study heat transfer at the interior surfaces of a building’s envelope. Convection heat transfer processes for various blind angles, HVAC regimes, surface temperatures, and window sizes are examined. Results show that convection at window and blind surfaces is highly dependent on blind angle, supply temperature and flow rate, moderately dependent on room-supply air temperature difference and HVAC regime, and weakly dependent on surface-supply air temperature difference. A simplified model of convection heat transfer in this particular situation is proposed for easy implementation in energy modeling software. / text

An energy analysis was performed on the Texas A & M University at Qatar building in Doha, Qatar. The building and its HVAC systems were modeled using EnergyPlus. Building chilled water and electrical data were collected to validate the computer simulation. The simulated monthly electricity consumption was within plus/minus 5 percent of the metered building data. Ninety-five percent of simulated hourly electricity data in a day were within plus/minus 10 percent of metered data. Monthly chilled water demand was within plus/minus 18 percent of measurements, and simulated monthly demand was correlated to metered monthly values with an R-squared correlation coefficient of 0.95. Once the simulation was verified with the metered data, an optimization of the building's HVAC systems was performed. Better utilizing the building's variable speed fans at part loads showed potential annual electricity savings of 16 percent over the base case, with another 22 percent savings in chilled water energy. After converting chilled water savings to equivalent chiller electricity savings, the potential utility cost savings over the base case were found to be $90,000/yr at local utility rates. Reducing outdoor air intake to ASHRAE indoor air quality minimums yielded an additional 17 percent in potential chilled water savings and brought total monetary savings over the base case to $110,000/yr. Using a dedicated outside air system to precisely control individual zone ventilation showed potential for an additional 12 percent chilled water savings and $14,000 in yearly utility savings, while also eliminating cases of under-ventilation. A hypothetical retrofit of fan powered terminal units (FPTU's) resulted in energy savings only at very low minimum flow rates, below ventilation standards. Savings were never more than 20 percent over the no-fan case. Series FPTU's showed no savings at any flow setting and negligible difference was found between ECM and SCR motor control. Finally, the dependence on climate of each improvement was studied. Simulations were run in the relatively milder climates of Houston and Phoenix and compared to those found for Doha. It was found that variable speed fan operation is a more cost effective option for milder climates, while outside air control is more cost effective in extreme hot and humid climates such as Doha. Future study is needed to make the FPTU model valid for different climates and flow ranges.

abstract: Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on off-line model is applied based on system identification and Kalman filtering methods. The developed data-driven modeling framework is validated on various genres of buildings, and the experimental results demonstrate desired performance on building energy forecasting in terms of accuracy and computational efficiency. The framework could be easily implemented into building energy model predictive control (MPC), demand response (DR) analysis and real-time operation decision support systems. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2016

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The global prominence of energy-efficient retrofit in the context of aging properties has garnered noteworthy attention. This surge in interest can be attributed to several advantages, encompassing economically viable carbon dioxide (CO₂) emissions reduction, diminished energy expenditures, and improved indoor air quality. Passive retrofits, such as thermal insulation and fenestration improvement, and active retrofits, such as heating setpoint temperature optimization, offer great potential for CO₂ reduction and energy savings. The central objective of this study is ascertaining the feasibility of attaining a 40% reduction in CO₂ emissions with the lowest cost and with constraints on heating setpoints temperature by finding optimal design parameters encompassing thermal insulation (including both single and double-layer), fenestration, and heating setpoint temperatures. This inquiry is substantiated through a case study of the Leblanc residence on the University of Ottawa campus. In pursuit of this objective, a thermal model of the Leblanc building was developed via EnergyPlus and subsequently subjected to a validation process following ASHRAE Guideline 14. After validation, an array of discrete optimization scenarios was executed using the NSGA-II model, facilitated by the JEPLUS+EA software. This approach aimed to identify the most suitable parameters for achieving optimal CO₂ reduction and cost outcomes. Notably, the results showcased 20 solutions, each boasting a reduction of 40% or more in CO₂ emissions and heating setpoint temperature higher than 18 °C. While the choice to prioritize either cost or CO₂ reduction remains at the user's discretion, four solutions have been discerned as the most effective. Furthermore, the findings suggest that implementing these optimal solutions can significantly decrease CO₂ emissions, ranging between 41.79% and 46.36%. The associated costs were also determined to fall within $36,262 to $57,934.

No description available.

abstract: City administrators and real-estate developers have been setting up rather aggressive energy efficiency targets. This, in turn, has led the building science research groups across the globe to focus on urban scale building performance studies and level of abstraction associated with the simulations of the same. The increasing maturity of the stakeholders towards energy efficiency and creating comfortable working environment has led researchers to develop methodologies and tools for addressing the policy driven interventions whether it’s urban level energy systems, buildings’ operational optimization or retrofit guidelines. Typically, these large-scale simulations are carried out by grouping buildings based on their design similarities i.e. standardization of the buildings. Such an approach does not necessarily lead to potential working inputs which can make decision-making effective. To address this, a novel approach is proposed in the present study. The principle objective of this study is to propose, to define and evaluate the methodology to utilize machine learning algorithms in defining representative building archetypes for the Stock-level Building Energy Modeling (SBEM) which are based on operational parameter database. The study uses “Phoenix- climate” based CBECS-2012 survey microdata for analysis and validation. Using the database, parameter correlations are studied to understand the relation between input parameters and the energy performance. Contrary to precedence, the study establishes that the energy performance is better explained by the non-linear models. The non-linear behavior is explained by advanced learning algorithms. Based on these algorithms, the buildings at study are grouped into meaningful clusters. The cluster “mediod” (statistically the centroid, meaning building that can be represented as the centroid of the cluster) are established statistically to identify the level of abstraction that is acceptable for the whole building energy simulations and post that the retrofit decision-making. Further, the methodology is validated by conducting Monte-Carlo simulations on 13 key input simulation parameters. The sensitivity analysis of these 13 parameters is utilized to identify the optimum retrofits. From the sample analysis, the envelope parameters are found to be more sensitive towards the EUI of the building and thus retrofit packages should also be directed to maximize the energy usage reduction. / Dissertation/Thesis / Masters Thesis Architecture 2017

Urban energy system planning can play a pivotal role in the transition of urban areas towards energy efficiency and carbon neutrality. With the building sector being one of the main components of the urban energy system, there is a great opportunity for improving energy efficiency in cities if the spatio-temporal patterns of energy use in the building sector are accurately identified. A bottom-up engineering energy model of buildings, known as urban building energy model (UBEM), is an analytical tool for modeling buildings on city-levels and evaluating scenarios for an energy-efficient built environment, not only on the building-level but also on the district and city-level. Methods for developing an UBEM vary, yet, the majority of existing models use the same approach to incorporating already established building energy simulation software into the main core of the model. Due to difficulties in accessing building-specific information on the one hand, and the computational cost of UBEMs on the other hand, simplified building modeling is the most common method to make the modeling procedure more efficient. This thesis contributes to the state-of-the-art and advancement of the field of urban building energy modeling by analyzing the capabilities of conventional building simulation tools to handle an UBEM and suggesting modeling guidelines on the zoning configuration and levels of detail of the building models. According to the results from this thesis, it is concluded that with 16% relative difference from the annual measurements, EnergyPlus is the most suitable software that can handle large-scale building energy models efficiently. The results also show that on the individual building-level, a simplified single-zone model results in 6% mean absolute percentage deviation (MAPD) from a detailed multi-zone model. This thesis proposes that on the aggregated levels, simplified building models could contribute to the development of a fast but still accurate UBEM.