Method for visualizing energy use in building information models

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9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK

Method for visualizing energy use in building information models The 15th International Symposium on District Heating and Cooling

Hanh Truonga, Abigail Franciscob, Ardalan Khosrowpoura, John E. Taylorb,*, Neda Assessing the feasibilityMohammadi of using bthe heat demand-outdoor

Department and Engineering, Virginia Patton VA States temperature a long-term district heat demand forecast Department of of Civil Civilfunction and Environmental Environmentalfor Engineering, Virginia Tech, Tech, 113B 113B Patton Hall, Hall, Blacksburg, Blacksburg, VA 24061, 24061, United United States

b b

aa

School of of Civil Civil and and Environmental Environmental Engineering, Engineering, Georgia Georgia Institute Institute of of Technology, Technology, 790 790 Atlantic Atlantic Dr Dr NW, NW, Atlanta, Atlanta, GA GA 30313, 30313, United United States States School

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

a

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c

Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France

While technology advancements are increasingly improving the energy efficiency of buildings, occupant behavior remains a critical factor in ensuring the effectiveness of such enhancements. To this end, numerous eco-feedback systems have been developed to reduce building energy use through adjusting occupants’ behaviors. The information represented in an eco-feedback system affects the users' engagement, motivation, and interpretation. In this paper, we introduce a new information representation method in which Abstract a building information model (BIM) is integrated with energy use information to enhance visual representation of energy use. The BIM-integrated visualization approach developed paper allows users to visualize energy consumption of each District heatingenergy networks are commonly addressed in in thethis literature as one of the most effective solutions for values decreasing the building room through a color-coding scheme in an as-built BIM. Colors correspond to the levels of energy consumption in greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat individual rooms compared other rooms in a building, which enables a visually intuitive comparison building ecosales. Due to the changedto climate conditions and building renovation policies, heatnormative demand in the futureforcould decrease, feedback systems. This representation may lead to increased user engagement in and improved interpretation of eco-feedback prolonging the investment return period. systems. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©forecast. 2017 TheThe Authors. by Elsevier districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 © 2017 The Authors. Published by Ltd. committee of the 9th International Conference on Applied Energy. Peer-review under responsibility of Elsevier the scientific buildings that vary in both construction periodcommittee and typology. weather scenarios (low,onmedium, and three district Peer-review under responsibility of the scientific of theThree 9th International Conference Applied high) Energy. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: building information modeling; eco-feedback; energy efficiency; information representation; normative Keywords: information energy efficiency; information representation; normative comparison. comparedbuilding with results from modeling; a dynamiceco-feedback; heat demand model, previously developed and validated by thecomparison. authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.scenarios, Introduction the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the The efficient use of of energy resources gained global as scarce resources arecombination being depleted, energy decrease in the number heating hours of has 22-139h during the attention heating season (depending on the of weather and renovation scenarios considered). On the other hand, function intercept increased 7.8-12.7% per decade39% (depending the costs rise, and greenhouse gas emissions continue to increase. In the Unitedfor States, approximately of the on total coupledconsumption scenarios). The values suggested could used to modify the function parameters for theactivities scenarios[1]. considered, and energy and its associated CO22 be emissions are attributed to building-related Occupant improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * * Corresponding author. Tel.: +1-404-894-8021. Corresponding author. Tel.: +1-404-894-8021. Cooling. E-mail address: address: [email protected] [email protected] E-mail

Keywords: Heat demand; Forecast; Climate change 1876-6102 © 1876-6102 © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. Peer-review Peer-review under under responsibility responsibility of of the the scientific scientific committee committee of of the the 9th 9th International International Conference Conference on on Applied Applied Energy. Energy.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.089

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behaviors in buildings are known to be critical to building energy efficiency [2-5], providing the potential to offset up to 7.4% of the associated CO2 emissions, according to one study [6]. Recently, researchers have examined ecofeedback systems, which provide energy feedback to building occupants on individual or group behaviors with a goal of reducing environmental impact [2, 6-8]. The use of eco-feedback systems is based in part on the premise that people lack awareness and understanding of how their everyday consumption behaviors affect the environment. Eco-feedback systems may bridge this gap by automatically sensing these behaviors through computerized means and communicating behavioral impacts to occupants [9, 10]. Although there are numerous factors attributed to the effectiveness of an eco-feedback system, an efficient user interaction with the system is an essential factor in achieving the goal of energy conservation [11, 12]. The efficacy of this interaction depends on two main factors: information content and visualization, and researchers continue to explore ways to create more effective and impactful information visualization techniques in eco-feedback systems. One recent promising trend is the combination of energy use information into building information models (BIMs) [13]. In the current practice, BIM is a widely used form of information storage and visualization in the architecture, engineering and construction (AEC) industry [14]. Most previous studies have focused on using BIM for energy efficiency in the early design phase [15-17], however, the potential of using BIM for energy efficiency and energy use visualization in the building operation phase is unexplored, beyond efforts involving simulated data [13]. This study explores employing BIMs as an energy use visualization framework in eco-feedback systems to achieve improved energy conservation outcomes. 2. Background To evaluate the effectiveness of residential and commercial building eco-feedback systems, the effects of underlying components such as information representation methods, psychological motivators, and interface design have been explored. These components have improved system effectiveness and resulted in energy savings, ranging from 5% to 55% [7, 18]. For example, normative comparison is a type of social comparison feedback method in which an individual or a group is compared to a norm (i.e., a reference value or benchmark), thereby using social norm as a motivation to encourage the conservation of energy. Several studies have demonstrated that the normative comparison component of eco-feedback systems can drive energy efficient behavior from users through competition and public perception [2, 10]. However, the results of eco-feedback studies are not entirely consistent regarding the effectiveness of information representation methods on occupants’ behaviors. For example, studies found people tended to understand the representation of energy in monetary units [19], however, other studies found monetary representation is not an effective approach to increase the occupants’ energy efficiency [20, 21]. In addition, studies show that building occupants often have difficulty in interpreting charts and understanding energy units [22, 23]. Recognizing the limitations of existing energy information representation, recent studies have used color-coding techniques to represent energy use [24]. Color-coding has been shown in other research domains to provide effective learning and interpretation due to increased retention and cognition performance [25-27]. In eco-feedback research domains, Bonino et al. [24] conducted interviews to assess users’ reactions to color-coded 2D floor plan energy consumption visualizations and found this color-based and spatial visualization helped 71.77 % of respondents understand energy consumption. However, this study had several shortcomings, including the lack of a control group and statistically validated results. Based on the findings and shortcomings in the Bonino et al. study, there is a compelling opportunity to evaluate user understanding of eco-feedback systems that incorporate color-based representations of energy use integrated into spatial building layouts, such as BIM, that are statistically evaluated with a control group. However, to address these opportunities, we first need methods to integrate energy consumption information into BIM, as well as methods to automate the representation of color-coded energy use information in BIM. Many studies have combined BIM with building energy simulation programs (e.g. EnergyPlus) to optimize the energy efficiency decision-making process and develop sustainable designs [15, 16, 28]. However, most of these studies have mainly focused on implementing BIM for energy efficiency in the design phase, whereas energy consumption during the operation phase is also critical. The operations phase is usually between 20 – 50 years and accounts for approximately 80% – 95% of the total energy consumption during building’s life [29]. Costa and colleagues [30] developed an integrated toolkit for building energy managers that links BIM and the monitoring, analysis and optimization of energy in a building during its operational phase. But we still lack a method to link



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energy consumption into a BIM in a visually intuitive, user-friendly manner that is targeted for occupant understanding. In this paper, we leverage an as-built BIM and develop a method that integrates energy consumption data into BIM using a novel color-based normative comparison approach. Our method may enhance the representation of energy use information for eco-feedback systems, while maintaining the psychological effect of the normative comparison on occupants’ behavior. The methods to develop this system are discussed in the following section. 3. Methodology We developed a Revit add-in using the Revit Application Programming Interface (API) to integrate a test-bed building’s energy consumption into the model through an automated color-coding approach. The developed add-in interprets the energy consumption data captured through smart meters and generates a color-coded visualization of the energy use levels in the as-built model. In the following subsections, the three main steps implemented to operationalize this method are explained. Step 1: Energy Data Collection. An approach that was compatible with the data collection setup described by Jain et al. [9] was developed to capture the energy consumption of each room in a building. The energy data was collected using transducers measuring current power flow installed in a test-bed building in New York City monitoring the current power flow of each individual room. Onset Computing HOBO U30 data loggers were used to record the Root Mean Square (RMS) amperage for each room. By multiplying the RMS amperage value by 110 V, the apparent power was then calculated and stored in a Structured Query Language (SQL) database. The stored data contained the daily energy use information for each room along with the associated unique ID to identify the room. Step 2: As-built Model Color Coding Procedure. Autodesk Revit 2014 was used to construct an as-built model for the test-bed building. For color-based energy visualization, each individual room was assigned an energy use level. To compute the energy use level, first the energy use intensity of each room was computed by dividing each room’s daily energy use by the room’s area. Next, energy use levels were computed by applying the maximum and minimum energy use intensity to the following equation,

 E  Emin   Ei Emin  i  max  8  

(1)

where Ei was the energy use intensity for level i, which varies from 0 to 8. Emax was the maximum energy use intensity in the selected data, and Emin was the minimum energy use intensity in the selected data. The energy use levels were associated with a color spectrum as shown in Table 1, enabling normative comparison of energy use intensity by room. The green, yellow, and red represent the low (E0), medium (E4), and high (E8) energy use levels, respectively. Table 1: Color Spectrum Setting Energy use Levels

E0

E1

E2

E3

E4

E5

E6

E7

E8

Color Spectrum

Two techniques for color-coded visualization of a BIM where a variable is mapped to a desired color spectrum were tested. First, the Revit Color Scheme function was used to color and apply fill patterns to the rooms in the BIM. This technique was simple; however, during certain time periods when the energy use intensity of a room was substantially different from others, one or more energy use levels were missing from the color spectrum in Table 1. In this event, Revit randomly re-assigned a new color spectrum. This is a drawback, as it would make it difficult for users visualizing this system to track their energy levels across time. Another Revit technique was tested that involved changing the color of a floor-finish element (e.g., adding a ceiling element with its height equal to zero created a floor-finish element). This technique overcame the drawback of the Color Scheme technique described above, and enabled the color spectrum to be viewed in 2D and 3D. To generate 3D color-coded visualizations, the same color needed to be applied to all elements in a room. In BIM, walls are normally painted using different colors. To overcome this, the split faces technique was applied on the floors and

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walls to show the energy use data of a room with a single color. We found split face techniques are flexible and can also be drawn on electrical equipment (e.g., a television, a microwave, or a bulb) to track their energy use data. Therefore, the developed method can be refined for different purposes in information representation or tracking energy use data from a macro-scale, such as a room in a building, to a micro-scale, such as a microwave in that room. Step 3: BIM-energy Integration Approach. We developed a BIM-EN (BIM-integrated energy use) add-in to integrate the energy consumption data and color-coded energy use levels into the as-built BIM. We developed the add-in using the Revit API and C#. In the following paragraphs, the three main steps required to execute the task of color-coding and displaying energy use levels in BIM are explained in detail. First, input data was organized in a matrix form and stored in a text file to be read and processed using C#. The input data contains the room IDs, material IDs, and the daily energy use for a specific month. The material ID holds the color for the split-faced element discussed in Step 2. Second, the BIM-EN allows users to select a specific date range from the input data for which they would like to generate visualization. The selected energy use intensity data are then averaged over the requested date range by room and stored using a variable called Energy_Norm. They are then classified into 9 different energy use levels with identical intervals using Eq. 1. These energy use levels are stored using an Energy_Level variable by BIM-EN. Third, to color-code each room we retrieve the room’s ID, which is a unique BuiltInParameter from the as-built model. BIM-EN then matches the retrieved ID with the Room ID to get the corresponding material ID and the energy use intensity in the Energy_Norm variable. The color of the material assigned to the room is then accessed through a BuiltInParameter called MATERIAL_PARAM_COLOR and changed to a specified color as illustrated in Table 1. In addition to displaying the energy use level color, the room name and corresponding energy use intensity value can be displayed in the visualization. The functionality of BIM-EN was validated by creating a set of pseudo energy consumption values and manually calculating each room’s corresponding colors. The same data was then imported into our software to automatically color-code the 2D and 3D models. Fig. 1 displays this information on the color-coded 2D and 3D models. The BIMEN-generated results matched our manually developed ground truth, and the represented information and colors were accurate in both the 2D and 3D BIM-based energy consumption visualizations.

Room 10 Fig. 1. Normative color-based visualization of energy consumption level in the 2D and 3D as-built model

4. Discussion Information representation in eco-feedback systems has been recognized as one of the most significant factors impacting users’ energy conservation [2]. Several studies have demonstrated that representing energy consumption using normative comparison can drive energy efficient behavior by users [2, 10]. In addition, many studies have focused on improving the effectiveness of information representation by investigating different representation units like direct energy units, environmental or monetary units, or integrating various virtual aids like images, art, or avatars



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[18, 31, 32]. Other studies have focused on color-coding techniques, with results showing color-coded information is effective at increasing human retention and enhancing the human learning process and awareness [26, 27, 33]. Bonino et al. [24] used different colors to present energy consumption spatially in a 2D floor plan and 71.77% of viewers found the color-based visualization helpful due to its efficiency of locating corresponding information to attract their attention. The method developed in this paper builds on Bonino et al. by proposing an approach to create similar spatially oriented and color-coded energy visualizations by employing an existing industry standard approach, BIM [14]. This novel approach enables 2D and 3D spatial visualization of energy use values in an as-built BIM using a color-based normative comparison representation. Our method balances aesthetic interest [22] and effective data representation by providing both accurate energy values for each room along with normative color-coded visualization in 2D and 3D. This allows the BIM-energy integration visualization method to exploit the benefits of an engaging user interface without compromising information accuracy. In our approach, we used the split faces on floors and walls to show the energy use data of rooms; however, a split face can also be drawn on equipment (e.g., a television, a microwave, or a refrigerator). Thus, an additional contribution of our approach facilitates a general and comprehensive method, which enables the simultaneous 2D and 3D color-coding of not only building elements, but also all plugged-in appliances in BIMs. The BIM-integrated visualization method developed in this paper has broader implications in other fields such as water, gas, comfort, indoor environment quality, and building maintenance feedback systems. Such a visualization method not only can be implemented to provide feedback with the necessary level of detail, but also can facilitate decision making through faster interpretation and intuitive understanding of building energy statuses. There are some limitations to this approach that should be addressed in future research. The energy use values are not automatically imported into the as-built model, therefore the BIM energy use visualization is not yet dynamic [34]. In addition, to visualize energy use levels that correspond to expected colors, a step of making split faces and including the paint material for each room in Revit is required. Future research is needed to explore to what extent this visualization can improve the effectiveness of eco-feedback systems in enhancing user understanding, decision making, and energy consumption behavior. As mentioned above, a previous study has empirically evaluated energy use displays using color-coded 2D floor plans [24], however the study lacked a control group and statistically validated results. Therefore, the method developed in this paper should be empirically evaluated and build on previous studies by including a control group and statistical validated results. This empirical analysis would also add to previous research by assessing 3D in addition to 2D spatial energy representation. Studying user engagement and understanding of the BIM-based energy use visualization method described in this paper will provide an indication of its potential to impact energy consumption behavior and improve building energy efficiency. 5. Conclusions In most eco-feedback systems research, energy consumption information has been represented to users using different charts and technical units, which are not easily comprehensible to many users [19, 22-24]. The BIM-energy integration method developed in this paper provides a novel information representation method that enables spatial energy consumption visualization using different colors in an as-built BIM. These color-coded models create an aesthetically interesting user interface and may increase user engagement and interpretation of energy data, potentially impacting energy consumption behaviors. The integration of a color-based normative comparison energy visualization into a BIM model represents an integral step toward deepening our understanding of the impact of eco-feedback information representation on fostering pro-environmental behaviors. Acknowledgement This material is based upon work supported by the National Science Foundation under Grant No. 1142379. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors would like to also thank the Vietnam Education Foundation for providing funding to support the efforts of the first author.

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