02052nas a2200217 4500008003900000245008100039210006900120260005100189520129700240100001601537700002401553700002001577700002501597700001801622700001901640700002101659700002201680700002101702700002701723856008401750 2015 d00aGreen, Clean, & Mean: Pushing the Energy Envelope in Tech Industry Buildings0 aGreen Clean Mean Pushing the Energy Envelope in Tech Industry Bu bLawrence Berkeley National Laboratoryc05/20153 a

When it comes to innovation in energy and building performance, one can expect leading-edge activity from the technology sector. As front-line innovators in design, materials science, and information management, developing and operating high-performance buildings is a natural extension of their core business.

The energy choices made by technology companies have broad importance given their influence on society at large as well as the extent of their own energy footprint. Microsoft, for example, has approximately 250 facilities around the world (30 million square feet of floor area), with significant aggregate energy use of approximately 4 million kilowatt-hours per day.

There is a degree of existing documentation of efforts to design, build, and operate facilities in the technology sector. However, the material is fragmented and typically looks only at a single company, or discrete projects within a company.Yet, there is no single resource for corporate planners and decision makers that takes stock of the opportunities and documents sector-specific case studies in a structured manner. This report seeks to fill that gap, doing so through a combination of generalized technology assessments (“Key Strategies”) and case studies (“Flagship Projects”).

1 aMills, Evan1 aGranderson, Jessica1 aChan, Wanyu, R.1 aDiamond, Richard, C.1 aHaves, Philip1 aNordman, Bruce1 aMathew, Paul, A.1 aPiette, Mary, Ann1 aRobinson, Gerald1 aSelkowitz, Stephen, E. uhttps://simulationresearch.lbl.gov/publications/green-clean-mean-pushing-energy01686nas a2200133 4500008004100000050001500041245005900056210005700115300001200172490000800184520125400192100002001446856008601466 2009 eng d aLBNL-2077E00aGeneric Optimization Program User Manual Version 3.0.00 aGeneric Optimization Program User Manual Version 300 a380-3910 v1133 a

A software tool that automates the analysis of functional tests for air-handling units is described. The tool compares the performance observed during manual tests with the performance predicted by simple models of the components under test that are configured using design information and catalog data. Significant differences between observed and expected performance indicate the presence of faults. Fault diagnosis is performed by analyzing the variation of these differences with operating point using expert rules and fuzzy inferencing.

The tool has a convenient user interface to facilitate manual entry of measurements made during a test. A graphical display compares the measured and expected performance, highlighting significant differences that indicate the presence of faults. The tool is designed to be used by commissioning providers conducting functional tests as part of either new building commissioning or retro-commissioning, as well as by building owners and operators conducting routine tests to check the performance of their HVAC systems. The paper describes the input data requirements of the tool, the software structure, the graphical interface, and summarizes the development and testing process used.

1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/generic-optimization-program-user00440nas a2200109 4500008004100000245006000041210006000101260002000161100002900181700003100210856008900241 2008 eng d00aGebäudesimulation mit adaptiven Modellierungsansätzen0 aGebäudesimulation mit adaptiven Modellierungsansätzen aKassel, Germany1 aNytsch-Geusen, Christoph1 aNouidui, Thierry, Stephane uhttps://simulationresearch.lbl.gov/publications/geb%C3%A4udesimulation-mit-adaptiven00576nas a2200133 4500008004100000245013000041210006900171260002800240100002100268700001900289700002400308700002100332856008900353 2007 eng d00aGlobal Efficiency of Direct Flow Vacuum Collectors in Autonomous Solar Desiccant Cooling: Simulation and Experimental Results0 aGlobal Efficiency of Direct Flow Vacuum Collectors in Autonomous aBeijing, Chinac09/20071 aBourdoukan, Paul1 aWurtz, Etienne1 aSpérandio, Maurice1 aJoubert, Patrice uhttps://simulationresearch.lbl.gov/publications/global-efficiency-direct-flow-vacuum01826nas a2200157 4500008004100000245010000041210006900141260002800210520122700238100002701465700001801492700002601510700002401536700002701560856008101587 2006 eng d00aA Guide for Specifying Performance Monitoring Systems in Commercial and Institutional Buildings0 aGuide for Specifying Performance Monitoring Systems in Commercia aSan Francisco, CAc20063 a

This paper describes a guide for specifying performance monitoring systems that was developed as part of jointly funded CEC PIER-DOE project intended to assist commercial and institutional building owners in specifying what is required to obtain the information necessary to initiate and sustain an ongoing commissioning activity. The project's goal was to facilitate the delivery of specific performance related information to the benefit of both commissioning providers and building operators. A number of large-building owners were engaged in order to help create 'market pull' for performance monitoring while producing a specification that met their needs. The specification guide and example specification language addresses four key aspects of performance monitoring:

The paper describes key aspects of the guide including how measurement accuracy requirements relate to the performance metrics that are used in both troubleshooting and routine reporting. Guide development activities and related tech-transfer efforts are also presented.

1 aGillespie, Kenneth, L.1 aHaves, Philip1 aHitchcock, Robert, J.1 aDeringer, Joseph, J1 aKinney, Kristopher, L. uhttps://simulationresearch.lbl.gov/publications/guide-specifying-performance01766nas a2200109 4500008004100000022001500041245004800056210004400104520140400148100002001552856008401572 2004 eng d aLBNL-5419900aGenOpt 2.0.0 - Generic optimization program0 aGenOpt 200 Generic optimization program3 a

GenOpt is an optimization program for the minimization of a cost function that is evaluated by an external simulation program. It has been developed for optimization problems where the cost function is computationally expensive and its derivatives are not available or may not even exist. GenOpt can be coupled to any simulation program that reads its input from text files and writes its output to text files. The independent variables can be continuous variables (possibly with lower and upper bounds), discrete variables, or both, continuous and discrete variables. Constraints on dependent variables can be implemented using penalty or barrier functions.

GenOpt has a library with local and global multi-dimensional and one-dimensional optimization algorithms, and algorithms for doing parametric runs. An algorithm interface allows adding new minimization algorithms without knowing the details of the program structure.

GenOpt is written in Java so that it is platform independent. The platform independence and the general interface make GenOpt applicable to a wide range of optimization problems.

GenOpt has not been designed for linear programming problems, quadratic programming problems, and problems where the gradient of the cost function is available. For such problems, as well as for other problems, special tailored software exists that is more efficient.

1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/genopt-200-generic-optimization01717nas a2200133 4500008004100000245005400041210005300095260002700148520125500175100002301430700002501453700001801478856008701496 2004 eng d00aGraph-theoretic Methods in Simulation Using SPARK0 aGraphtheoretic Methods in Simulation Using SPARK aArlington, VAc04/20043 aThis paper deals with simulation modeling of nonlinear, deterministic, continuous systems. It describes how the Simulation Problem Analysis and Research Kernel (SPARK) uses the mathematical graph both to describe models of such systems, and to solve the embodied differential-algebraic equation systems (DAEs). Problems are described declaratively rather than algorithmically, with atomic objects representing individual equations and macro objects representing larger programming entities (submodels) in a smooth hierarchy. Internally, in a preprocessing step, graphs are used to represent the problem at the level of equations and variables rather than procedural, multi-equation blocks. Benefits obtained include models that are without predefined input and output sets, enhancing modeling flexibility and code reusability, and relieving the modeler from manual algorithm development. Moreover, graph algorithms are used for problem decomposition and reduction, greatly reducing solution time for wide classes of problems. After describing the methodology the paper presents results of benchmark tests that quantify performance advantages relative to conventional methods. In a somewhat contrived nonlinear example we show O performance as opposed1 aSowell, Edward, F.1 aMoshier, Michael, A.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/graph-theoretic-methods-simulation00495nas a2200133 4500008004100000245005400041210005300095260003800148100002300186700002500209700001800234700002000252856008900272 2004 eng d00aGraph-Theoretic Methods in Simulation Using SPARK0 aGraphTheoretic Methods in Simulation Using SPARK aArlington, Virginia, USAc04/20041 aSowell, Edward, F.1 aMoshier, Michael, A.1 aHaves, Philip1 aCurtil, Dimitri uhttps://simulationresearch.lbl.gov/publications/graph-theoretic-methods-simulation-002364nas a2200169 4500008004100000245004400041210004200085260001900127300001200146490000600158520188400164100002002048700002202068700002602090700001602116856006202132 2001 eng d00aGenOpt - A Generic Optimization Program0 aGenOpt A Generic Optimization Program aRio de Janeiro a601-6080 vI3 a

The potential offered by computer simulation is often not realized: Due to the interaction of system variables, simulation users rarely know how to choose input parameter settings that lead to optimal performance of a given system. Thus, a program called GenOpt® that automatically determines optimal parameter settings has been developed.

GenOpt is a generic optimization program. It minimizes an objective function with respect to multiple parameters. The objective function is evaluated by a simulation program that is iteratively called by GenOpt. In thermal building simulation — which is the main target of GenOpt — the simulation program usually has text-based I/O. The paper shows how GenOpt's simulation program interface allows the coupling of any simulation program with text based I/O by simply editing a configuration file, avoiding code modification of the simulation program. By using object-oriented programming, a high-level interface for adding minimization algorithms to GenOpt's library has been developed. We show how the algorithm interface separates the minimization algorithms and GenOpt's kernel, which allows implementing additional algorithms without being familiar with the kernel or having to recompile it. The algorithms can access utility classes that are commonly used for minimization, such as optimality check, line-search, etc.

GenOpt has successfully solved various optimization problems in thermal building simulation. We show an example of minimizing source energy consumption of an office building using EnergyPlus, and of minimizing auxiliary electric energy of a solar domestic hot water system using TRNSYS. For both examples, the time required to set up the optimization was less than one hour, and the energy savings are about 15%, together with better daylighting usage or lower investment costs, respectively.

1 aWetter, Michael1 aLamberts, Roberto1 aNegrão, Cezar, O. R.1 aHensen, Jan uhttp://www.ibpsa.org/proceedings/BS2001/BS01_0601_608.pdf00482nas a2200109 4500008004100000245011700041210006900158260002700227100001800254700001800272856008200290 1986 eng d00aGeneration of Data for Passive Solar Building Simulation from a Three Dimensional Architectural Modelling System0 aGeneration of Data for Passive Solar Building Simulation from a  aPecs, Hungaryc09/19861 aGreen, Cedric1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/generation-data-passive-solar