A Real-time Building HVAC Model Implemented as a Plug-in for Trimble SketchUp Zaker A. Syed 1 and Thomas H. Bradley 2

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1 A Real-time Building HVAC Model Implemented as a Plug-in for Trimble SketchUp Zaker A. Syed 1 and Thomas H. Bradley 2 1 Graduate Research Assistant, Department of Mechanical Engineering, Colorado State University; 1584 Campus Delivery, Fort Collins, CO (corresponding author) Phone: (970) Zaker.Syed@colostate.edu 2 Associate Professor, Department of Mechanical Engineering, Colorado State University, 1584 Campus Delivery, Fort Collins, CO Thomas.Bradley@colostate.edu Abstract: One of the greatest challenges faced by humanity is the impact on climate due to human activity. Among the most prominent of these activities is the construction of buildings. As time and technology progress, the need for more efficient and environmentally-friendly processes and materials is gaining favor among the general population. In line with this, various government protocols have been implemented which have led to development of various software applications that assist in designing better buildings. However, the reach of these applications is limited owing to the cost and expertise needed to actually use these software applications. For instance, architects have multiple tools at their disposal for designing an energy efficient building. But most of these tools are costly to operate, time-consuming and require extensive and software-specific training. This has a greater impact in the initial stages of design, where the amount of data is very limited. The focus of this paper is to develop a plugin for Google SketchUp, which is the CAD design software most commonly used by architects, so as to give insight into building energy consumption during the initial design stages. The challenge is to develop a calculation methodology that is sufficiently accurate, requires very little data from the user, requires no prior training to use, and consumes less time than the state of the art simulation methods. The main component of energy consumption modeled using the developed software is the Heating, Ventilation and Air Conditioning [HVAC] operation. The HVAC system is modeled using design and analysis methods developed by ASHRAE [Radiant Time Series], coupled with reallife time series solar data from NSRDB. This toolset will help architects evaluate design features like the placement and size of fenestration, materials for construction, building orientation, and more, to enable early design decisions to improve the building s lifecycle energy consumption. Introduction Construction of commercial buildings is one of the primary activities that impact the environment and ecosystem. With the objective towards reducing the impact of buildings on the environment, various government and industrial programs and incentives have been developed to enable the construction of highly efficient buildings. An example of the reach of these reforms was demonstrated in Hasegawa in which 25% of new office buildings in UK had obtained an environmental assessment label [1]. Many methods and tools have been developed by researchers

2 and industry organizations to facilitate engineers and architects in designing energy efficient buildings. However, there are various barriers that have limited the utility of these tools within a conventional architecting and engineering design process. Specifically, these tools are generally high cost, require considerable expertise, require a considerable time commitment, and are not suited to the early stages of building design (when detailed design information is not available). To develop a toolset that would have more utility to the early stages of building design, we have considered the general design framework presented by Malmqvist et.al. [2], and repeated here as Figure 1. The design of a building usually starts with a project that is assigned to a company by a customer. During the initial interactions the customer gives his requirements to the designers using which they come up with an initial design. Early in the design process, many design options are available, but the amount of knowledge that the designer has available to them is low. Figure 1 General relationship between design options and Knowledge But as time progresses, the number of design options reduce until it becomes impractical to make any more changes to the design. On the other hand, the accuracy of results as well as the available knowledge increases with time. Hence better decisions could be taken at this point, but available options are reduced because earlier design decisions have been locked in. One way around this issue is to have tools that can develop knowledge earlier in the design process. The coincidence of high knowledge and a large number of design options is hypothesized to lead to better design decisions. The plugin being developed in this project aims to provide more knowledge to the architects in the preliminary design phase. By developing an energy modeling toolset that is compatible with the tools, constraints, and methods of early building architecting and design, early design decisions can be made effectively to improve building energy efficiency in the final product. Design decisions based on HVAC system Minor selections in a design can have a major impact on the end energy consumption of a building after its completion. For instance, the appropriate orientation of a building can reduce

3 energy consumption by 30-40% [3]. Similarly proper sizing and positioning of fenestration of a building also have a major impact on HVAC loads, and therefore total energy consumption. In general practice, architects use rules of thumb and previous experience to design these features. For example, it is common knowledge that windows facing east and west have maximum solar gain. Hence to reduce solar heat gain generally windows are avoided on these faces, and if used, proper shading is applied. Some of the early design decisions that architects can take to reduce energy impact include: Building orientation Window properties like size, shading and glazing type Wall insulation material Previous Tools Numerous tools have been developed to assist designers in making energy efficient buildings. But many of these tools require some previous training and technical know-how about the software and its limitations. Google SketchUp is a software package that is widely used by architects for generating initial CAD models of buildings. Though EnergyPlus has a plugin for SketchUp, it requires the entire model to be constructed again in the plugin and has considerable computational costs to run each simulation. The computational cost of such programs both increases the time required for design, and makes these tools incompatible with early stages of sketching, architecting and design. One specific tool that follows the same simulation methodology as this plugin is the Green Building Studio developed by Autodesk. This tool has the benefit of being able to import data from any CAD software using gbxml and exporting the data into energy plus to carry out a detailed simulation and also takes into account the real world weather data accumulated over more than a million virtual weather stations [4]. However a major concern while using this software is the unreliability of gbxml while converting geometry. A study by Stanford University [5] shows that some geometry/wall properties are lost during conversion. In terms of computing power requirements, since this tool utilizes cloud computing, it is more robust but is dependent on the use of internet. Whereas, the tool in development does not require file conversion and gives the preliminary simulation results in the same CAD environment, thereby allowing an architect to make the first few decisions in a more informed way. Thus this plugin aims at changing the general work flow of design by integrating the simulation stage within the CAD environment and reduce the time consumed in the numerous detailed iterations needed to reach an optimal solution. Figure 2 compares the usual process flow with the modified process flow. Hence having a plugin that uses the existing CAD data to give instantaneous preliminary results within the same environment/computer window would help in removing many of the barriers inhibiting the application of energy modeling tools.

4 Figure 2 - Process Flow of Design The rest of the paper from this point on will discuss the modelling procedures developed by ASHRAE and how it was integrated with the National Solar Radiation Database [NSRDB]. Design inputs Before selecting the modelling techniques it is essential to know what inputs are available to us for the design process. Google SketchUp uses the programming language Ruby for its Application Programming Interface (API). Using Ruby scripts, we were able to extract the following data from a CAD model Wall/Window/Floor/Roof areas Wall orientation angle with respect to south(azimuth angle) GPS location of site from Google Earth All the above data is already generated by default when an architect creates his CAD model. The only extra input that the user has to provide is the building type as per the 16 DoE classification of commercial buildings [6]. Based on the building type, material properties are pulled from a database that is hard-coded into the plugin. Figure 3 - SketchUp CAD model of a prototypical commercial building

5 With these readily available inputs, we seek to model the HVAC energy consumption of the building. The ASHRAE Radiant Time Series (RTS) method was selected for its robustness to limited input information, and because it takes into account the weather data of a selected location. Radiant Time Series (RTS) American Society of Heating Refrigeration and Air conditioning Engineers (ASHRAE) has done extensive research in the field of HVAC load calculation and developed various calculations methods. Out of all these methods, the Heat Balance (HB) method is the most exhaustive and state of the art approach. It is also the method used by EnergyPlus to model energy consumption of buildings. But due to the information and computation time constraints, the Radiant Time Series (RTS) method was selected. RTS method is a simplified approach derived from HB method to calculate HVAC peak load calculations. Due to its inherent assumptions of steady-periodic conditions [7], it was unsuitable for doing annual energy calculations. To counter the effect of this assumption, real time data from NSRBD TMY3 was implemented in place of peak load values of weather conditions. Figure 4. Flow chart for RTS method including NSRDB-informed hourly load calculations A general overview of the RTS procedure is depicted in Figure 3 [5]. The first step in the RTS procedure involves calculating the solar intensities for each hour for each exterior surface. The NSRBD TMY3 dataset consists of hourly values of direct and diffused irradiance on a horizontal surface for a typical year [8]. By transposing these values for a vertical wall in each of the standard 8 orientations along the geographic axes, the transmitted and diffused heat gains were

6 calculated and a database was created for all the TMY3 locations. Table 2 shows the net solar irradiance for the city of Denver for first 10 daylight hours of the year for the standard orientations. Table 1 - Net direct and diffused irradiance for site (Denver Intl. Airport) from NSRDB TMY3 Date Time DNI (W/m 2 ) DHI (W/m 2 ) 1/1/1995 7: /1/1995 8: /1/1995 9: /1/ : /1/ : /1/ : /1/ : /1/ : /1/ : /1/ : /1/ : /1/ : Table 2 Total Irradiance including direct, diffused and reflected components for each wall orientation in Btu/h.ft 2 Hour N NE E SE S SW W NW Effect of building orientation on HVAC load For the demonstration of the calculation, consider a medium sized office located in the city of Denver. The outer shell has a dimension of 120 by 80 and consists of 3 floors of 10 each. Two possible orientations are depicted in Figure 5. As can be seen from Figure 6 and Table 2 the max heat gain occurs at the south face and min heat gain on the north [Graph only shows the direct beam component of solar radiation].

7 Figure 5 - Orientation option one and two Taking this into account an architect can decide to orient the face with more window area towards the east-west direction to reduce cooling load of the building. Alternatively he/she may choose to provide shading on the other faces. Figure 6 - Direct Normal Irradiance The plugin under development calculates these values and presents it to the user for design consideration. Some of the other inputs which the user may change include window type, shading, wall color etc. Working method of the plugin The plugin pulls up the solar intensities for various wall orientations shown in table 2 and multiplies it with the surface areas that are extracted from the CAD model. It then applies the radiant time series to calculate the hourly load for the entire year. After adding the internal heat gains the plugin displays the result within the same window and keeps updating the result for each change made. Since the calculations consists only of multiplications the simulation time is in seconds. Though the results may not be as accurate as the ones obtained using the higher fidelity software packages, it gives an idea to the designer whether the change made to the design would improve the building or vice versa. He/she can then select the top five designs and send them for further analysis in more sophisticated software packages.

8 Conclusions This paper has presented the methods by which a light-weight building HVAC energy model is being developed from the ASHRAE RTS method and integrated into a Google Sketchup Plug in. The goal of the plugin is not to develop a calculation model, but to develop a new workflow for an architect that lets him/her make better informed design decisions. Once some preliminary designs are selected using this plugin, they can be run into more standardized and accurate software packages to reach a final decision. This toolset has advantages over the current state of the art in building HVAC system modeling tools in that it has fewer information requirements, lower computational time requirements, presents the results within the CAD environment, low training time, and can be readily integrated into the conventional workflow of building architects. It is anticipated that these types of tools will find utility in enabling the design and development of more energy efficient commercial and residential buildings. Future work will concentrate on integrating other aspects of the energy consumption of the entire building lifecycle in the plug in. Through integration of materials embedded energy calculations, HVAC energy consumption, lighting energy consumption [Daylight utilization], water consumption, and end-of-life, a building carbon footprint metric tool can be developed. References [1] Hasegawa, T Policy Instruments for Environmentally Sustainable Buildings, Proc., CIB/iiSBE Int. Conf. on Sustainable Building2002, CD-ROM, EcoBuild, Oslo, Norway [2] Malmqvist, T Life cycle assessment in buildings: The ENSLIC simplified method and guidelines [3] Cofaigh EO, Fitzgerald E, Alcock R, McNicholl A, Peltonen V, Marucco A. A green Vitruvius - principles and practice of sustainable architecture design. London: James & James (Science Publishers) Ltd; [4] [5] Tobias Maile et. al Building Energy Performance Simulation Tools - a Life-Cycle and Interoperable Perspective - CIFE Working Paper #WP107 December 2007 [6] Deru, M. et.al U.S. Department of Energy Commercial Reference Building Models of the National Building Stock [7] ASHRAE 2013 Handbook - Fundamentals [8] Compiled by National Renewable Energy Laboratory (NREL)