IBS Plug and Play Tools are the Challenge in Creating IBS Products • Many separate modeling tools exist now – Air flow modeling – Thermal loads modeling – Control & systems modeling • Next generation of integrated products require an integrated research and development tool The National Institute for Science and Technology Building Fire Research Laboratory (NIST BFRL) has developed an excellent air dispersion modeling system called CONTAM for simulating fires in buildings. However this system does not take into consideration the effects of heating and cooling system and the control system on the air flow in the building. The US Department of Energy has created DOE-2 to model energy usage in buildings. However, the tool is meant to estimate hourly loads and does not consider the detailed functioning of controllers and systems such as the HVAC system. In addition it does not produce data for faulted conditions. NIST created HVACSIM+ & U. Wisonconsin created TRNSYS to model the detailed functioning of HVAC systems including faulted operation. However, these systems do not include variable air flow volumes or reverse flows and are not easily integrated with control system models. Control system vendors have several proprietary controls simulation systems (SIMULINK is built with Mat. Lab). However, these systems are difficult to integrated with air flow, thermal, control, systems, and the new Plug & Play infrastructure we are developing. Air Flow Modeling (e. g. , CONTAM) Loads Modeling (e. g. , DOE-2) Systems Modeling (e. g. , HVACSIM+) Controls Modeling (e. g. , SIMULINK) Integrated Modeling (e. g. , IBS Plug & Play)
The IBS Plug & Play Modeling System • Provides an integrated building modeling environment to model interactions between various systems in building in great detail. • Can model faulty behavior and its impacts. • Expandable from air, thermal, and control system to include other elements such as lights, equipment, and processes. • Provide interfaces to users such as operators, vendors, and service providers. Plug and Plug Modeling Window The Plug & Play system integrates many building modeling tools. This allows IBS product developers to build Plug & Play-enabled demonstrations, prototypes, and products. IBS product developers need data that represent both faulted and un-faulted building operations. Software tools based on learning systems such as neural networks and genetic algorithms are an effective means to quickly developing building system diagnosticians, but they must have access to a very large volume of reliable examples of both good and bad behavior before they can “learn” to detect problems in building systems. The current Plug & Play system can only handle airside systems and controllers. The infrastructure is open. Before a complete suite of IBS product can be developed to support the Plug & Play system, it must be extended to emulate and control other systems such as hot and cold water distribution, chiller/boiler plants, steam, electricity, lights, plugs, etc. Plug & Play systems will have different user interfaces for different users. For example, building operators will have an operations console from which they might override control signals. Control vendors will have a controls design console with which they can specify the control scheme and set points for the devices which are found on the control network. Service providers will have specialized consoles (such as the commissioning console picture below) with which they will provide services to building owners. Controls Vendor Interface Service Provider Interface Simulation methodology Plug & Play emulation handles variable sub-second simulation time-steps for individual components (intracomponent) and 1 -second simulation time-steps for system level interactions (inter-component) Windows-based network implementation The Plug and Play system is implement in an extended computational environment implemented on a minimum of one Windows 95/98/NT-based computer. More computers may be used if desired to implement commissioning system, diagnostic systems, etc. Systems Operator Interface Implementation of faulted behavior Development extensibility and compatibility Faulted behavior can be introduced into any component modeled in the system. For example a sensor can fail to return a signal at all, or it can return an incorrect signal. Or flow directions can reverse from the expected. The effect of these faults will affect the other components in the system appropriately. The Plug & Play system is created using Microsoft Visual C++ and SWI-Prolog. Communications protocols are implemented using TCP/IP and Windows sockets. The Plug & Play system is compatible with the Whole-Building Diagnostician’s BASlink protocol. The Plug & Play system appears on networks as a normal building control system.
IBS Plug & Play Product Application Areas • Enable development and detailed evaluation of energy savings of IBS products that rely on benefits of integrating systems. • Advance the state of the art in control systems – Enable next generation control in which commissioning and diagnostic are integrated. – Automated control code generation from heating/cooling system design specification. – Generate compiled control code to reduce size and cost of controllers. • Create training data required to quickly implement effective neural-net and genetic algorithm-based diagnosticians • Enable development of advanced and automated commissioning procedures and tools. Today we cannot quantify the positive effects of IBS technologies on the energy performance, occupant health, and environmental impact of buildings. Plug and Play development tools will permit the evaluation of the economic and health benefits of IBS technologies. The most advanced control code development system envisioning so far relies heavily on the existence of a Plug and Play systems. Soon building designers will specify what equipment they want and how they’d like it work. Based on this design information, a suite of Plug and Play tools will automatically generate the control code, automated commissioning test procedures, and diagnosticians without human intervention. The result software would be deployed more quickly, more reliably, and at lower cost than present-day control systems. Today’s control code relies on interpreters instead of compilers. Compilers produce much more compact and reliable code than code written for interpreters. Smaller and more reliable code fits on smaller, lowercost controllers. Diagnostician can be created manually but only a great cost. A few diagnosticians have been created using automated methods but they have generally either been less reliable or unable to explain their decisions. The available of more and better training data will enable the development of better neural network-based diagnostician and the development of the a new type of genetic algorithm-based diagnostician capable of providing explanations and cause isolation.
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