UT’s Tech Team Moves the Needle Toward Building Optimization

Values at Work Series: Facilities Services developed our core values of Service, Integrity, Teamwork, Excellence, and Stewardship (collectively referred to as SITES) based on the way we approach our work. Our values can be found throughout our areas of services in many ways.Through a series of articles, the Values at Work Series is devoted to providing a closer look at how our values are hard at work. Each article focuses on an area of service and how that group incorporates our values in a significant way. We are proud of our employees who continue to serve as an example of “walking the talk” every day.After facilities managers address their client’s unplanned maintenance issues, provide the necessary preventive maintenance on building systems and manage energy conservation efforts, how do they also find a way to optimize the performance of the buildings on campus so the systems run as well as they ought to...and do it all with limited resources? Building Operations Manager Mark White provides insights into how The University of Texas at Austin, with the help of its Building Optimization Team (BOT), is moving the needle toward impactful facilities optimization on a university campus.Facilities Service

Study on k-shortest paths with behavioral impedance domain from the intermodal public transportation system perspective

Behavioral impedance domain consists of a theory on route planning for pedestrians, within which constraint management is considered. The goal of this paper is to present the k-shortest path model using the behavioral impedance approach. After the mathematical model building, optimization problem and resolution problem by a behavioral impedance algorithm, it is discussed how behavioral impedance cost function is embedded in the k-shortest path model. From the pedestrian's route planning perspective, the behavioral impedance cost function could be used to calculate best subjective paths in the objective way.Postprint (published version

Efficient Genetic Algorithm sets for optimizing constrained building design problem

The main aim of this paper is to find the appropriate set of Genetic Algorithm (GA), control parameters that attain the optimum, or near optimum solutions, in a reasonable computational time for constrained building optimization problem. Eight different combinations of control parameters of binary coded GA were tested in a hypothetical building problem by changing 80 variables. The results showed that GA performance was insensitive to some GA control parameter values such as crossover probability and mutation rate. However, population size was the most influential control parameter on the GA performance. In particular, the population sizes (15 individuals) require less computational time to reach the optimum solution. In particular, a binary encoded GA with relatively small population sizes can be used to solve constrained building optimization problems within 750 building simulation calls

Influence of Uncertainty in User Behaviors on the Simulation-Based Building Energy Optimization Process and Robust Decision-Making

Computer-based simulations have been widely used to predict building performances. Building energy simulation tools are generally used to perform parametric studies. However, the building is a complex system with a great number of variables. This leads to a very high computational cost. Therefore, using a building optimization algorithm coupled with an energy simulation tool is a more promising solution. In this study, EnergyPlus is connected to a genetic algorithm that uses a probabilistic search technique based on evolutionary principles. Various sources of uncertainty exist in simulation-based building optimization problems. This study aims to investigate the influence of occupant behavior-related input variables on the optimization process. To integrate the uncertainty into the optimization process, a stochastic approach using the Latin hypercube sampling (LHS) method is employed. The varying input variables are defined by the LHS method, and each sampling run generates 14 samples. Five optimization parameters are used, and the recommendations for parameter settings of each parameter are generated as the optimization result. It is important to provide a decision maker with a decision-making framework to support robust decision-making from the generated recommendations. A clear or relatively clear tendency of recommendations toward a particular parameter setting is observed for three parameters. For these three parameters, the frequency of recommendation is identified to be a good indicator for the robustness of the most recommended setting. The test of proportion is performed to investigate the statistical significance between parameter settings. For the other two parameters, recommendations are comparatively evenly distributed among parameter settings, and the statistical significance is not shown. In this case, the Hurwicz decision rule is utilized to select an optimal solution. This dissertation contributes to the field of building optimization as it proposes a method to integrate uncertainty in input variables and shows the method generates reliable results. Computational time is reduced by using the LHS method compared to the case of using a random sampling method. While this study does not include all potential input variables with uncertainties, it provides significant insight into the role of input variables with uncertainty in the building optimization process.PHDArchitectureUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135836/1/nuri_1.pd

Profile of the first generation of marketing expert systems

The emergence of expert systems in marketing can be seen as the next step in the development of the use of computers in marketing management, where starting out with an almost exclusively mathematical model building/optimization approach, gradually more judgmental elements from managerial experience were added (decision calculus; marketing decision support systems)

An Efficient and Accurate Building Optimization Strategy Using Singular Value Decomposition

Optimizing the life cycle cost of a building typically involves a large number of variables due to the many options that exist at the time that a building is being designed. Such large-scale optimization problems are often prohibitive within the building industry because of the excessive computational time required by the building energy modeling software; therefore, any optimization studies that are performed during a building design are typically only completed using a small number of variables. To achieve the goal of performing a full life cycle building optimization in an acceptable time frame, this paper proposes an accurate and efficient method using singular value decomposition on the design variables. Through the use of singular value decomposition a large number of design variables can be reduced to a smaller subset of design variables that can be solved more quickly by the optimization algorithm. In this paper the authors apply the singular value decomposition method to a case study of a typical residential building in six separate locations across the U.S. and compare the results with those of the full optimization process over the entire design space

CSA C873 Building Energy Estimation Methodology - A simplified monthly calculation for quick building optimization

CSA C873 Building Energy Estimation Methodology (BEEM) is a new series of (10) standards that is intended to simplify building energy calculations. The standard is based upon the German DIN Standard 18599 that has 8 years of proven track record and has been modified for the Canadian market. The BEEM method relies on steady state heat balance equations using monthly averages instead of dynamic equations used in hourly software. The method then relies on utilization factors to calculate the contribution of heat gain on heating loads and includes a simplified algorithm for lighting savings associ-ated with daylight strategies. The daylight algorithm is based on avail-able climate data and detailed daylight modelling. The method was validated through the modelling of seven building archetypes in 6 dif-ferent climate zones. Results from the BEEM modeling is compared to similar buildings modeled in CanQuest. Seven typical building ar-chetypes were modeled in 6 different Canadian climate zones. These archetypes are different than the ASHRAE 140 or the BESTEST models with more zones defined and different HVAC systems. The in-tent was to compare the method for typical simple Canadian commer-cial buildings. An average of 8.5% difference on the overall energy consumption was found. Acknowledging there is a difference between energy modeling software results, this difference needs to be put in perspective with differences between energy modeling software and difference from energy modelling to real building consumption. BEEM has the advantage of offering a direct feedback to the user al-lowing for a real time optimization process. The intent of this method is to provide a tool for simple buildings that usually don't get modeled. The BEEM method is not intended as a replacement for the more de-tailed energy modelling simulation that is typically performed for larger or more complex buildings. The planned release date for the standard is March 2014. The CSA C873 Building Energy Estimation Methodology task force is considering the development of a software tool to assist with the adoption of BEEM for simple projects. The Na-tional research Council – Canadian Codes Centre is considering the standard as a path for demonstrating compliance with the National Energy Code for Buildings in 2015