@article {60932, title = {Green, Clean, \& Mean: Pushing the Energy Envelope in Tech Industry Buildings}, year = {2015}, month = {05/2015}, publisher = {Lawrence Berkeley National Laboratory}, abstract = {

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 ({\textquotedblleft}Key Strategies{\textquotedblright}) and case studies ({\textquotedblleft}Flagship Projects{\textquotedblright}).

}, author = {Evan Mills and Jessica Granderson and Wanyu R. Chan and Richard C. Diamond and Philip Haves and Bruce Nordman and Paul A. Mathew and Mary Ann Piette and Gerald Robinson and Stephen E. Selkowitz} } @article {1445, title = {Generic Optimization Program User Manual Version 3.0.0}, volume = {113}, number = {Part 1}, year = {2009}, pages = {380-391}, abstract = {

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.

}, author = {Michael Wetter} } @proceedings {236, title = {Geb{\"a}udesimulation mit adaptiven Modellierungsans{\"a}tzen}, journal = {BAUSIM 2008}, year = {2008}, address = {Kassel, Germany}, author = {Christoph Nytsch-Geusen and Thierry Stephane Nouidui} } @conference {2663, title = {Global Efficiency of Direct Flow Vacuum Collectors in Autonomous Solar Desiccant Cooling: Simulation and Experimental Results}, booktitle = {Proc. Building Simulation 2007}, year = {2007}, month = {09/2007}, address = {Beijing, China}, author = {Paul Bourdoukan and Etienne Wurtz and Maurice Sp{\'e}randio and Patrice Joubert} } @proceedings {270, title = {A Guide for Specifying Performance Monitoring Systems in Commercial and Institutional Buildings}, journal = {14th National Conference on Building Commissioning}, year = {2006}, month = {2006}, address = {San Francisco, CA}, abstract = {

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{\textquoteright}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 {\textquoteright}market pull{\textquoteright} 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.

}, author = {Kenneth L. Gillespie and Philip Haves and Robert J. Hitchcock and Joseph J Deringer and Kristopher L. Kinney} } @article {53, title = {GenOpt 2.0.0 - Generic optimization program}, year = {2004}, abstract = {

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.

}, issn = {LBNL-54199}, author = {Michael Wetter} } @proceedings {285, title = {Graph-theoretic Methods in Simulation Using SPARK}, journal = {High Performance Computing Symposium of the Advanced Simulation Technologies Conference}, year = {2004}, month = {04/2004}, address = {Arlington, VA}, abstract = {This 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 opposed}, author = {Edward F. Sowell and Michael A. Moshier and Philip Haves} } @proceedings {2834, title = {Graph-Theoretic Methods in Simulation Using SPARK}, journal = {High Performance Computing Symposium of the Advanced Simulation Technologies Conference (Society for Modeling Simulation International)}, year = {2004}, month = {04/2004}, address = {Arlington, Virginia, USA}, author = {Edward F. Sowell and Michael A. Moshier and Philip Haves and Dimitri Curtil} } @proceedings {50, title = {GenOpt - A Generic Optimization Program}, journal = {Proc. of the 7th IBPSA Conference}, volume = {I}, year = {2001}, pages = {601-608}, address = {Rio de Janeiro}, abstract = {

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{\textregistered} 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 {\textemdash} which is the main target of GenOpt {\textemdash} the simulation program usually has text-based I/O. The paper shows how GenOpt{\textquoteright}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{\textquoteright}s library has been developed. We show how the algorithm interface separates the minimization algorithms and GenOpt{\textquoteright}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.

}, url = {http://www.ibpsa.org/proceedings/BS2001/BS01_0601_608.pdf}, author = {Michael Wetter}, editor = {Roberto Lamberts and Cezar O. R. Negr{\~a}o and Jan Hensen} } @proceedings {354, title = {Generation of Data for Passive Solar Building Simulation from a Three Dimensional Architectural Modelling System}, journal = {PLEA{\textquoteright}86 Conference}, year = {1986}, month = {09/1986}, address = {Pecs, Hungary}, author = {Cedric Green and Philip Haves} }