- Email: [email protected]
Designing community-scale energy feedback
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)4178–4183 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Designing community-scale energy feedback
The 15th International Symposium on District Heating and Cooling Designing community-scale energy feedback
Abigail Franciscoaa, John E. Taylora* Abigail Francisco , John the E. Taylor feasibility of using heata*demand-outdoor
Assessing the temperature function for a long-term district heat demand forecast
School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr NW, Atlanta, GA 30313, United States a School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr NW, Atlanta, GA 30313, United States a
a,b,c
a
a
b
c
c
Abstract I. Andrić *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre Abstract a IN+systems Center for Technology Policy Research -networks Instituto Superior Técnico, Av.community-scale Rovisco Pais 1, 1049-001 Portugal Energy areInnovation, evolving from largeand scale, centralized to decentralized, energyLisbon, systems, within b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France energy systems, within Energycitizens systems are evolving from large scale, centralized networks to decentralized, community-scale which are expected to have much more central and proactive roles. Knowing that levels of citizen involvement can heavily c Département Systèmes Environnement - IMTroles. Atlantique, 4 rue that Alfred Kastler, 44300 Nantes, Francecan heavily which citizens are expected to haveÉnergétiques much more etcentral and projects, proactive Knowing levels involvement influence the success and efficiency of community energy research is exploring meansoftocitizen boost community-scale citizen influence the within successenergy and efficiency community building-level energy projects, research to boostavailable community-scale participation systems. of Concurrently, energy dataisisexploring becomingmeans increasingly city-wide, citizen which participation energy systems. Concurrently, building-level is becoming increasingly available city-wide, which has potential within to increase citizen learning and engagement with theenergy energydata performance in their community. However, research on has potential to increase citizen learning and engagement with the energy performance in their community. However, research on how to make this data accessible, understandable, and useful to citizens is limited. This study applies findings from energy feedback Abstract how to make this datathe accessible, understandable, useful tosystem citizensatisthe limited. This study applies findings from feedback literature to propose design of a novel energyand feedback community scale. We demonstrate theenergy principles that literature todesign propose of a novel energy feedback at the community scale. We demonstrate the inform of the ourdesign community energy feedback in presenting theoffrontand back-end of a prototype system. that We Districttheheating networks are commonly addressed insystem the system literature as one the most effective solutions forprinciples decreasing the inform thewith design of our community energylimitations, feedback system in future presenting the frontand back-end of returned a prototype system. conclude a discussion of thisthe system’s planssystems for research, and potential to improve citizen through understanding, greenhouse gas emissions from building sector. These require high investments which are the We heat conclude withand discussion of climate this system’s limitations, plans forrenovation future research, andand, potential to improve citizen engagement, behaviors with energy systems inand orderbuilding to achieve community-scale, by extension, urban scale,understanding, energy goals. sales. Due toa the changed conditions policies, heat demand in the future could decrease, engagement, and behaviors with energy systems in order to achieve community-scale, and, by extension, urban scale, energy goals. prolonging the investment return period. Copyright © 2018 Elsevier Ltd. All rights reserved. main scope of this paper isby to Elsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©The 2019 The Authors. Published Ltd. Copyright © 2018 Elsevier Ltd. All rights reserved. International Conference Applied Energy Selection and peer-review responsibility the scientific committee of theas10ath case forecast. The district of under Alvalade, located inofLisbon (Portugal), was used study. The district on is consisted of 665 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) th International Conference on Applied Energy Selection and peer-review under responsibility of the scientific committee of the 10 (ICAE2018). buildings that vary in both construction periodcommittee and typology. Three weather scenarios (low, medium, high) and threeEnergy. district Peer-review under responsibility of the scientific of ICAE2018 – The 10th International Conference on Applied (ICAE2018). renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: behavior change; community energy systems; energy feedback; urban energy efficiency compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: behavior change; community energy systems; energy feedback; urban energy efficiency
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). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the In response limited resources, impending populations, energy decrease in the to number of fossil heatingfuel hours of 22-139h during theclimate heatingchange season impacts, (dependingand on growing the combination of weather and In response to limited fossil fuel resources, impending climate impacts, and generation growing populations, energy systems worldwide are experiencing aother radical shift. Large-scale, centralized power is (depending giving way renovation scenarios considered). On the hand, function interceptchange increased for 7.8-12.7% per decade on to the systems are values experiencing radical shift. centralized power for generation is giving way and to coupled worldwide scenarios). The suggested could be[1]. usedThis toLarge-scale, modify thebring function parameters the scenarios considered, decentralized, community-scale energyasystems shift can broad benefits to communities by increasing decentralized, community-scale energy systems [1]. This shift can autonomy, bring broadwhile benefits to communities by increasing improve theimproving accuracy of energy heat demand estimations. local jobs, efficiency, and promoting energy simultaneously helping national
local jobs, improving energy efficiency, and promoting energy autonomy, while simultaneously helping national © 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. * Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . * Corresponding Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 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 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.812
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Abigail Francisco et al. / Energy Procedia 158 (2019) 4178–4183 Author name / Energy Procedia 00 (2018) 000–000
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entities meet energy reduction targets. Realizing the full potential of decentralized energy technologies will require much greater community understanding of and engagement with energy systems [1,2]. Concurrently, voluntary and mandated disclosure of building-level energy consumption data is becoming increasingly common in cities across the United States [3,4]. Providing this data in a way that is understandable, accessible, and actionable to a community’s citizens has the potential to improve community understanding and involvement with energy systems, and consequently boost the likelihood for such systems’ successful and efficient operation. In this paper, we leverage this data and find synergies from energy feedback literature to propose the design for a novel energy feedback system: community-level energy feedback. We develop a prototype application to demonstrate the principles that informed its design, and explore the front-end and back-end functionality of the prototype application. Finally, we discuss the implications for development of this system, areas for future improvement, and plans for the deployment and testing. 2. Background 2.1. Community energy systems overview As diverse energy sectors such as electricity, cooling, and heating are integrated at a more local level, energy generation will be relocated closer to the consumer, composing what is known as a community energy system [1]. Community energy systems have gained attention in recent years due to their potential to reduce carbon emissions and provide value to local citizens. Importantly, citizens are expected to have new roles within community energy systems, evolving from passive consumers to active prosumers [1,5]. In light of this, several studies have emphasized the critical need for citizen participation as we transition from our existing centralized energy infrastructure to community-scale energy systems, as levels of citizen involvement heavily influence the success of new energy projects in a community [1,2]. Community participation can improve the success of an energy project for a variety of reasons, including: increasing the dissemination and adoption of energy technologies [6], improving the acceptance of a project [7], helping designers incorporate social and environmental contexts into a project [8], and enhancing the design of the project or technology itself [9]. Without community understanding and input, community energy systems and new energy technologies are at risk of being not fully utilized, misused, or outright rejected. While community involvement is paramount for an energy project’s success, levels of citizen participation in such projects can vary substantially. Through a survey of 599 people in the Netherlands, Koirala et al. [10] studied factors that are associated with citizens’ willingness to participate with community energy systems and found community trust, community resistance, energy independence, environmental concern, and energy-related education to be the most important factors. Concurrently, broad research efforts are aiming to boost community involvement with energy systems. At the same time, voluntary and mandated disclosure of energy consumption data for buildings across communities is becoming increasingly common [3,4]. While this data can shed light on the actual energy operations across a community and may be useful to constituents involved in energy decisions, few research efforts have focused on how citizens can interact with and use this data for their benefit. This presents the opportunity to leverage community energy data to provide energy feedback to citizens and to study its effect on community understanding, engagement, and participation with energy systems. 2.2. Energy feedback systems overview Energy feedback systems have been widely implemented and studied in residential and commercial buildings. Such systems present building energy consumption data in an accessible way to occupants to help them contextualize energy use and improve energy behaviors, with a goal of reducing building energy consumption. Motivations for implementing energy feedback technology is grounded on the assumption that people generally lack awareness of how their behaviors impact energy use and that providing energy information can encourage pro-environmental behaviors [11]. The impacts of these systems have been promising; for example, one meta-study showed building energy use reduced by an average of 7.4% across 156 energy feedback deployments [12]. A wide variety of design strategies have been employed in such systems, and while some feedback features have repeatedly shown success (e.g., real-time feedback, interactive tools, and disaggregated end-uses), the scope of the vast majority of energy feedback studies has been limited to the individual building level [11].
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Several researchers have called for expanding energy feedback systems to include broader scopes of buildings and people at the community-scale [5,13,14]. Recognizing how energy systems are transitioning to decentralized, community-based systems, Geelen et al. [5] envisioned community-scale energy feedback would be a critical intermediary for supporting citizen’s emerging role as “co-providers”. In addition, Hargreaves [13] theorized that community energy feedback could engage diverse participants and help redistribute the burden of carbon reductions from being placed solely on the individual to being tied to the community as a whole. This has potential to help overcome barriers to behavior change noted in previous energy feedback studies, where participants became frustrated by systems appearing to place the blame on the individuals, resulting in disengagement [15]. Recently, Burchell et al. [16] introduced community-based communication and workshops into an energy feedback deployment and found even participants who did not attend the workshops were still motivated to change behaviors because of their existence. Energy feedback incorporating community elements shows great potential; however, there is a need for research to introduce the principles that drive the design for such systems. As our energy systems morph into smarter, decentralized systems that give greater control to users, it is critical for citizens to have a deeper understanding of the energy systems in their community to be able to effectively participate and engage with them. Community-scale energy feedback has the potential to improve citizen understanding of community energy systems, and for this reason this paper details the concepts and principles that inform the design of a community-scale energy feedback system. 3. Methods A prototype of a community-scale energy feedback system was built to demonstrate the principles that informed the design of the system. The Georgia Tech (GT) campus was used as a testbed community for designing the prototype. Diverse visualization strategies were employed to make the design more appealing to broad audiences, enabling users to view high-level information and access more detailed technical data where desired. Importantly, the prototype design was intended for use on a mobile device in order to enable flexibility with where the application can be accessed (e.g., home, work, or throughout the community), and to support broad adoption across a community (i.e., more Americans now have access to the internet through a smart phone than through a desktop or laptop computer [17]). The following paragraphs detail the front-end and back-end development of the prototype system. 3.1. Front-end development Front-end development of the prototype was implemented using Adobe XD. Adobe XD software supports development of interactive, medium-fidelity user-interface prototypes, which is ideal for gathering user feedback and iterating the design prior to developing a fully functional interface. The user-interface was designed to provide members of a community with layers of information about energy consumption of individual buildings and energy performance of the community as a whole. In addition, building energy performance is compared to community-wide energy goals. Navigation of the application is facilitated by the bottom gray bar (Figure 1), which navigates users across the three main functionalities of the application: Building view, Community view, and Goals view. Interactive functionality and access to real-time data have been shown to be effective strategies in energy feedback systems for encouraging pro-environmental behavior [11]. The Building view was designed to feature near real-time data (15-minute interval) with interactivity accomplished through augmented reality (AR). AR has been demonstrated to encourage learning by enabling users to explore technical information that is invisible in the real-world and engage with the spatial relationships contained in this information [18]. Traditionally, building energy use information is not visible to occupants. AR functionality in the Building view allows users to visualize building energy consumption while traveling through their community using their mobile phone. Energy consumption information is displayed via virtual objects placed in the real-world environment, shown in the left panel of Figure 1. The color of the icon, orange, indicates the level of energy performance. This is based on a 5-point color scale defined in the legend. To calculate a building’s energy performance level, color-based, normative comparison techniques were applied, following the methods comparable to Francisco et al. [19]. Viewing energy use in this manner enables users to compare energy performance relative to other buildings in their community, and spatially associate and interact with energy consumption through AR functionality. In addition, in [19] people reported that color-based, normative visualization
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techniques were more engaging and motivating to change energy behaviors compared to conventional energy bar charts. While the Building view connects users with a building’s energy use in physical space, the Community view facilitates understanding of energy consumption across the entire community from any location. This view is shown in the center panel of Figure 1. Consistency is an important practice across any application, and therefore the same color-based, normative comparison approach is applied in the Community view. The development of the Community view functionality was additionally motivated by a study in which spatial carbon mapping empowered a community to advocate for renewable energy sources [20]. Spatial maps can facilitate a broad understanding of energy performance and are useful to diverse stakeholders [20]. While normative comparison techniques have been demonstrated to motivate poor performers to improve energy behaviors, it can also cause the boomerang effect: an unintended phenomenon where already efficient users are less motivated to adopt more efficient behaviors after they learn they are performing better than average [21]. To combat this, the Goals tab adopts alternative means to motivate users to reduce their energy impact by leveraging communitybased communication and goals features. In this view, the energy goal for a community is presented; in Figure 1, a theoretical goal of 50% energy reductions is specified, which can be personalized based on a region’s energy commitment. Below, a graph shows the community’s progress towards meeting the goal. Users can select a building to compare the community’s performance with a building. Community-based language is also used throughout to emphasize community aspects of the project, which has improved engagement levels in other studies [16]. The prototype design allows for additional means of interactivity. Across Building, Community, and Goals views, users can adjust the fuel type included in the visualization and analysis. Previous studies have found that disaggregated feedback, typically in the form of appliance-specific feedback, can help users determine more specific actions to reduce energy use [11]. While appliance-specific data is not available across the GT community, nor is it practical to obtain this data across most communities, disaggregated energy data is measured at the fuel-level, including: electricity, heating, and cooling use. This level of breakdown may help users define more specific actions to improve consumption, while adding to the interactivity of the application (i.e., users can toggle between the fuel type). In addition, interactive functionality is also permitted across time; users can select to view the current energy performance, as well as the energy performance for the past month or year in the Building and Community views.
Fig. 1. Front-end design for the Building view (left), Community view (middle), and Goals view (right)
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3.2. Back-end development Augmented Reality (AR) functionality is critical for the deployment of the community energy feedback system. This section details the back-end development of the AR functionality in the Building view, described above. The application was built on Android, using Android SDK (v7.0, API level 24) and Java SDK (v9.0.4) to build and run the application. To implement the AR features, Unity (v2017.3.1) and Vuforia AR packages (SDK v7.1.34) were used. Building energy data were stored as .csv files and 5 color-coded icons were stored as .png files. When executing the application, first the application reads the .csv file with the energy data values through the Application.streamingAssetsPath. Next, it calculates the distance between the mobile device and the building. The center point of the building was used for the building location. A pre-programmed distance was defined so that an icon will only appear if the user is within a certain range. If the user is within the range, it converts the numeric energy value of the building into one of the 5 color-coded icons. If the user adjusts the time range of the energy data, a script runs to select a different texture for the image icon. To test the application worked as designed, the Console.Log() function was used to print variables and verify their values were as expected. In addition, prior to deployment on a mobile phone, the application was run on the laptop on which it was developed on to determine if it achieves all functionalities, especially the time range adjustments corresponding to the icon textures. After verifying its functionality through ConsoleLog() and laptop verification, the application was installed on an Android mobile device and tested on a building on GT’s campus. Since the Vuforia packages have some conflicts with the main camera of the application, we tested the camera independently. Finally, we walked towards the test building on campus while observing the open application on a mobile device to verify the icon appeared once we were a certain distance from the building. 4. Limitations and future research The community energy feedback prototype design presented above has several limitations that highlight areas to be addressed in future research. While the design can help users understand energy operations and performance across a community of buildings, it does little to tell users how to reduce their energy footprint. Typical of most communitylevel energy reporting, our energy consumption data is measured at the building level, making it extremely important to provide users with more specific actions to reduce energy use or participate with energy systems. Future iterations of the prototype will include functionality to support users’ ability to take actions to save energy (e.g., providing energy saving tips, notifications for community energy events, or social networking capabilities). In addition, the prototype will undergo usability testing during full-scale development of the application. The prototype described in this paper allows users to interactively test each function within the system, which can enable gathering of in-depth user feedback to iterate the design accordingly. Following user testing, we plan to integrate the back-end and frontend functionality into a fully functional application. Future research will deploy this system within a community and gather data on citizen understanding, engagement, and behaviors with community energy systems pre- and postdeployment. 5. Conclusion As our current centralized energy infrastructure evolves into decentralized, community-based systems, citizens will have more control and play an increasingly fundamental role in the operations of our energy infrastructure. Consequently, this will require a deeper level of understanding and engagement from citizens in order to realize the full benefits of community energy systems. With the uptick in reporting and disclosure of building-level energy data across communities, there is an opportunity for this data to be leveraged for public understanding and benefit. However, to accomplish this, this data must be shaped and presented to citizens in an understandable, accessible, and actionable way. This paper introduces the concept of community energy feedback systems, the principles that inform the design of such systems, and the potential of using these concepts to improve citizen understanding, engagement, and behaviors within community energy systems. In our increasingly technology-based and data-driven world, it is vital for research to explore ways to improve the accessibility and usefulness of technical information to the public.
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This paper expands energy feedback research by introducing a novel system, community-scale energy feedback, with the potential to expand public participation in energy systems while helping strive toward low-carbon communities. Acknowledgements This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650044. 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. References [1]
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ScienceDirect ScienceDirect
Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)4178–4183 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Designing community-scale energy feedback
The 15th International Symposium on District Heating and Cooling Designing community-scale energy feedback
Abigail Franciscoaa, John E. Taylora* Abigail Francisco , John the E. Taylor feasibility of using heata*demand-outdoor
Assessing the temperature function for a long-term district heat demand forecast
School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr NW, Atlanta, GA 30313, United States a School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr NW, Atlanta, GA 30313, United States a
a,b,c
a
a
b
c
c
Abstract I. Andrić *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre Abstract a IN+systems Center for Technology Policy Research -networks Instituto Superior Técnico, Av.community-scale Rovisco Pais 1, 1049-001 Portugal Energy areInnovation, evolving from largeand scale, centralized to decentralized, energyLisbon, systems, within b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France energy systems, within Energycitizens systems are evolving from large scale, centralized networks to decentralized, community-scale which are expected to have much more central and proactive roles. Knowing that levels of citizen involvement can heavily c Département Systèmes Environnement - IMTroles. Atlantique, 4 rue that Alfred Kastler, 44300 Nantes, Francecan heavily which citizens are expected to haveÉnergétiques much more etcentral and projects, proactive Knowing levels involvement influence the success and efficiency of community energy research is exploring meansoftocitizen boost community-scale citizen influence the within successenergy and efficiency community building-level energy projects, research to boostavailable community-scale participation systems. of Concurrently, energy dataisisexploring becomingmeans increasingly city-wide, citizen which participation energy systems. Concurrently, building-level is becoming increasingly available city-wide, which has potential within to increase citizen learning and engagement with theenergy energydata performance in their community. However, research on has potential to increase citizen learning and engagement with the energy performance in their community. However, research on how to make this data accessible, understandable, and useful to citizens is limited. This study applies findings from energy feedback Abstract how to make this datathe accessible, understandable, useful tosystem citizensatisthe limited. This study applies findings from feedback literature to propose design of a novel energyand feedback community scale. We demonstrate theenergy principles that literature todesign propose of a novel energy feedback at the community scale. We demonstrate the inform of the ourdesign community energy feedback in presenting theoffrontand back-end of a prototype system. that We Districttheheating networks are commonly addressed insystem the system literature as one the most effective solutions forprinciples decreasing the inform thewith design of our community energylimitations, feedback system in future presenting the frontand back-end of returned a prototype system. conclude a discussion of thisthe system’s planssystems for research, and potential to improve citizen through understanding, greenhouse gas emissions from building sector. These require high investments which are the We heat conclude withand discussion of climate this system’s limitations, plans forrenovation future research, andand, potential to improve citizen engagement, behaviors with energy systems inand orderbuilding to achieve community-scale, by extension, urban scale,understanding, energy goals. sales. Due toa the changed conditions policies, heat demand in the future could decrease, engagement, and behaviors with energy systems in order to achieve community-scale, and, by extension, urban scale, energy goals. prolonging the investment return period. Copyright © 2018 Elsevier Ltd. All rights reserved. main scope of this paper isby to Elsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©The 2019 The Authors. Published Ltd. Copyright © 2018 Elsevier Ltd. All rights reserved. International Conference Applied Energy Selection and peer-review responsibility the scientific committee of theas10ath case forecast. The district of under Alvalade, located inofLisbon (Portugal), was used study. The district on is consisted of 665 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) th International Conference on Applied Energy Selection and peer-review under responsibility of the scientific committee of the 10 (ICAE2018). buildings that vary in both construction periodcommittee and typology. Three weather scenarios (low, medium, high) and threeEnergy. district Peer-review under responsibility of the scientific of ICAE2018 – The 10th International Conference on Applied (ICAE2018). renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: behavior change; community energy systems; energy feedback; urban energy efficiency compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: behavior change; community energy systems; energy feedback; urban energy efficiency
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). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the In response limited resources, impending populations, energy decrease in the to number of fossil heatingfuel hours of 22-139h during theclimate heatingchange season impacts, (dependingand on growing the combination of weather and In response to limited fossil fuel resources, impending climate impacts, and generation growing populations, energy systems worldwide are experiencing aother radical shift. Large-scale, centralized power is (depending giving way renovation scenarios considered). On the hand, function interceptchange increased for 7.8-12.7% per decade on to the systems are values experiencing radical shift. centralized power for generation is giving way and to coupled worldwide scenarios). The suggested could be[1]. usedThis toLarge-scale, modify thebring function parameters the scenarios considered, decentralized, community-scale energyasystems shift can broad benefits to communities by increasing decentralized, community-scale energy systems [1]. This shift can autonomy, bring broadwhile benefits to communities by increasing improve theimproving accuracy of energy heat demand estimations. local jobs, efficiency, and promoting energy simultaneously helping national
local jobs, improving energy efficiency, and promoting energy autonomy, while simultaneously helping national © 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. * Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . * Corresponding Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 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 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.812
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entities meet energy reduction targets. Realizing the full potential of decentralized energy technologies will require much greater community understanding of and engagement with energy systems [1,2]. Concurrently, voluntary and mandated disclosure of building-level energy consumption data is becoming increasingly common in cities across the United States [3,4]. Providing this data in a way that is understandable, accessible, and actionable to a community’s citizens has the potential to improve community understanding and involvement with energy systems, and consequently boost the likelihood for such systems’ successful and efficient operation. In this paper, we leverage this data and find synergies from energy feedback literature to propose the design for a novel energy feedback system: community-level energy feedback. We develop a prototype application to demonstrate the principles that informed its design, and explore the front-end and back-end functionality of the prototype application. Finally, we discuss the implications for development of this system, areas for future improvement, and plans for the deployment and testing. 2. Background 2.1. Community energy systems overview As diverse energy sectors such as electricity, cooling, and heating are integrated at a more local level, energy generation will be relocated closer to the consumer, composing what is known as a community energy system [1]. Community energy systems have gained attention in recent years due to their potential to reduce carbon emissions and provide value to local citizens. Importantly, citizens are expected to have new roles within community energy systems, evolving from passive consumers to active prosumers [1,5]. In light of this, several studies have emphasized the critical need for citizen participation as we transition from our existing centralized energy infrastructure to community-scale energy systems, as levels of citizen involvement heavily influence the success of new energy projects in a community [1,2]. Community participation can improve the success of an energy project for a variety of reasons, including: increasing the dissemination and adoption of energy technologies [6], improving the acceptance of a project [7], helping designers incorporate social and environmental contexts into a project [8], and enhancing the design of the project or technology itself [9]. Without community understanding and input, community energy systems and new energy technologies are at risk of being not fully utilized, misused, or outright rejected. While community involvement is paramount for an energy project’s success, levels of citizen participation in such projects can vary substantially. Through a survey of 599 people in the Netherlands, Koirala et al. [10] studied factors that are associated with citizens’ willingness to participate with community energy systems and found community trust, community resistance, energy independence, environmental concern, and energy-related education to be the most important factors. Concurrently, broad research efforts are aiming to boost community involvement with energy systems. At the same time, voluntary and mandated disclosure of energy consumption data for buildings across communities is becoming increasingly common [3,4]. While this data can shed light on the actual energy operations across a community and may be useful to constituents involved in energy decisions, few research efforts have focused on how citizens can interact with and use this data for their benefit. This presents the opportunity to leverage community energy data to provide energy feedback to citizens and to study its effect on community understanding, engagement, and participation with energy systems. 2.2. Energy feedback systems overview Energy feedback systems have been widely implemented and studied in residential and commercial buildings. Such systems present building energy consumption data in an accessible way to occupants to help them contextualize energy use and improve energy behaviors, with a goal of reducing building energy consumption. Motivations for implementing energy feedback technology is grounded on the assumption that people generally lack awareness of how their behaviors impact energy use and that providing energy information can encourage pro-environmental behaviors [11]. The impacts of these systems have been promising; for example, one meta-study showed building energy use reduced by an average of 7.4% across 156 energy feedback deployments [12]. A wide variety of design strategies have been employed in such systems, and while some feedback features have repeatedly shown success (e.g., real-time feedback, interactive tools, and disaggregated end-uses), the scope of the vast majority of energy feedback studies has been limited to the individual building level [11].
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Several researchers have called for expanding energy feedback systems to include broader scopes of buildings and people at the community-scale [5,13,14]. Recognizing how energy systems are transitioning to decentralized, community-based systems, Geelen et al. [5] envisioned community-scale energy feedback would be a critical intermediary for supporting citizen’s emerging role as “co-providers”. In addition, Hargreaves [13] theorized that community energy feedback could engage diverse participants and help redistribute the burden of carbon reductions from being placed solely on the individual to being tied to the community as a whole. This has potential to help overcome barriers to behavior change noted in previous energy feedback studies, where participants became frustrated by systems appearing to place the blame on the individuals, resulting in disengagement [15]. Recently, Burchell et al. [16] introduced community-based communication and workshops into an energy feedback deployment and found even participants who did not attend the workshops were still motivated to change behaviors because of their existence. Energy feedback incorporating community elements shows great potential; however, there is a need for research to introduce the principles that drive the design for such systems. As our energy systems morph into smarter, decentralized systems that give greater control to users, it is critical for citizens to have a deeper understanding of the energy systems in their community to be able to effectively participate and engage with them. Community-scale energy feedback has the potential to improve citizen understanding of community energy systems, and for this reason this paper details the concepts and principles that inform the design of a community-scale energy feedback system. 3. Methods A prototype of a community-scale energy feedback system was built to demonstrate the principles that informed the design of the system. The Georgia Tech (GT) campus was used as a testbed community for designing the prototype. Diverse visualization strategies were employed to make the design more appealing to broad audiences, enabling users to view high-level information and access more detailed technical data where desired. Importantly, the prototype design was intended for use on a mobile device in order to enable flexibility with where the application can be accessed (e.g., home, work, or throughout the community), and to support broad adoption across a community (i.e., more Americans now have access to the internet through a smart phone than through a desktop or laptop computer [17]). The following paragraphs detail the front-end and back-end development of the prototype system. 3.1. Front-end development Front-end development of the prototype was implemented using Adobe XD. Adobe XD software supports development of interactive, medium-fidelity user-interface prototypes, which is ideal for gathering user feedback and iterating the design prior to developing a fully functional interface. The user-interface was designed to provide members of a community with layers of information about energy consumption of individual buildings and energy performance of the community as a whole. In addition, building energy performance is compared to community-wide energy goals. Navigation of the application is facilitated by the bottom gray bar (Figure 1), which navigates users across the three main functionalities of the application: Building view, Community view, and Goals view. Interactive functionality and access to real-time data have been shown to be effective strategies in energy feedback systems for encouraging pro-environmental behavior [11]. The Building view was designed to feature near real-time data (15-minute interval) with interactivity accomplished through augmented reality (AR). AR has been demonstrated to encourage learning by enabling users to explore technical information that is invisible in the real-world and engage with the spatial relationships contained in this information [18]. Traditionally, building energy use information is not visible to occupants. AR functionality in the Building view allows users to visualize building energy consumption while traveling through their community using their mobile phone. Energy consumption information is displayed via virtual objects placed in the real-world environment, shown in the left panel of Figure 1. The color of the icon, orange, indicates the level of energy performance. This is based on a 5-point color scale defined in the legend. To calculate a building’s energy performance level, color-based, normative comparison techniques were applied, following the methods comparable to Francisco et al. [19]. Viewing energy use in this manner enables users to compare energy performance relative to other buildings in their community, and spatially associate and interact with energy consumption through AR functionality. In addition, in [19] people reported that color-based, normative visualization
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techniques were more engaging and motivating to change energy behaviors compared to conventional energy bar charts. While the Building view connects users with a building’s energy use in physical space, the Community view facilitates understanding of energy consumption across the entire community from any location. This view is shown in the center panel of Figure 1. Consistency is an important practice across any application, and therefore the same color-based, normative comparison approach is applied in the Community view. The development of the Community view functionality was additionally motivated by a study in which spatial carbon mapping empowered a community to advocate for renewable energy sources [20]. Spatial maps can facilitate a broad understanding of energy performance and are useful to diverse stakeholders [20]. While normative comparison techniques have been demonstrated to motivate poor performers to improve energy behaviors, it can also cause the boomerang effect: an unintended phenomenon where already efficient users are less motivated to adopt more efficient behaviors after they learn they are performing better than average [21]. To combat this, the Goals tab adopts alternative means to motivate users to reduce their energy impact by leveraging communitybased communication and goals features. In this view, the energy goal for a community is presented; in Figure 1, a theoretical goal of 50% energy reductions is specified, which can be personalized based on a region’s energy commitment. Below, a graph shows the community’s progress towards meeting the goal. Users can select a building to compare the community’s performance with a building. Community-based language is also used throughout to emphasize community aspects of the project, which has improved engagement levels in other studies [16]. The prototype design allows for additional means of interactivity. Across Building, Community, and Goals views, users can adjust the fuel type included in the visualization and analysis. Previous studies have found that disaggregated feedback, typically in the form of appliance-specific feedback, can help users determine more specific actions to reduce energy use [11]. While appliance-specific data is not available across the GT community, nor is it practical to obtain this data across most communities, disaggregated energy data is measured at the fuel-level, including: electricity, heating, and cooling use. This level of breakdown may help users define more specific actions to improve consumption, while adding to the interactivity of the application (i.e., users can toggle between the fuel type). In addition, interactive functionality is also permitted across time; users can select to view the current energy performance, as well as the energy performance for the past month or year in the Building and Community views.
Fig. 1. Front-end design for the Building view (left), Community view (middle), and Goals view (right)
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3.2. Back-end development Augmented Reality (AR) functionality is critical for the deployment of the community energy feedback system. This section details the back-end development of the AR functionality in the Building view, described above. The application was built on Android, using Android SDK (v7.0, API level 24) and Java SDK (v9.0.4) to build and run the application. To implement the AR features, Unity (v2017.3.1) and Vuforia AR packages (SDK v7.1.34) were used. Building energy data were stored as .csv files and 5 color-coded icons were stored as .png files. When executing the application, first the application reads the .csv file with the energy data values through the Application.streamingAssetsPath. Next, it calculates the distance between the mobile device and the building. The center point of the building was used for the building location. A pre-programmed distance was defined so that an icon will only appear if the user is within a certain range. If the user is within the range, it converts the numeric energy value of the building into one of the 5 color-coded icons. If the user adjusts the time range of the energy data, a script runs to select a different texture for the image icon. To test the application worked as designed, the Console.Log() function was used to print variables and verify their values were as expected. In addition, prior to deployment on a mobile phone, the application was run on the laptop on which it was developed on to determine if it achieves all functionalities, especially the time range adjustments corresponding to the icon textures. After verifying its functionality through ConsoleLog() and laptop verification, the application was installed on an Android mobile device and tested on a building on GT’s campus. Since the Vuforia packages have some conflicts with the main camera of the application, we tested the camera independently. Finally, we walked towards the test building on campus while observing the open application on a mobile device to verify the icon appeared once we were a certain distance from the building. 4. Limitations and future research The community energy feedback prototype design presented above has several limitations that highlight areas to be addressed in future research. While the design can help users understand energy operations and performance across a community of buildings, it does little to tell users how to reduce their energy footprint. Typical of most communitylevel energy reporting, our energy consumption data is measured at the building level, making it extremely important to provide users with more specific actions to reduce energy use or participate with energy systems. Future iterations of the prototype will include functionality to support users’ ability to take actions to save energy (e.g., providing energy saving tips, notifications for community energy events, or social networking capabilities). In addition, the prototype will undergo usability testing during full-scale development of the application. The prototype described in this paper allows users to interactively test each function within the system, which can enable gathering of in-depth user feedback to iterate the design accordingly. Following user testing, we plan to integrate the back-end and frontend functionality into a fully functional application. Future research will deploy this system within a community and gather data on citizen understanding, engagement, and behaviors with community energy systems pre- and postdeployment. 5. Conclusion As our current centralized energy infrastructure evolves into decentralized, community-based systems, citizens will have more control and play an increasingly fundamental role in the operations of our energy infrastructure. Consequently, this will require a deeper level of understanding and engagement from citizens in order to realize the full benefits of community energy systems. With the uptick in reporting and disclosure of building-level energy data across communities, there is an opportunity for this data to be leveraged for public understanding and benefit. However, to accomplish this, this data must be shaped and presented to citizens in an understandable, accessible, and actionable way. This paper introduces the concept of community energy feedback systems, the principles that inform the design of such systems, and the potential of using these concepts to improve citizen understanding, engagement, and behaviors within community energy systems. In our increasingly technology-based and data-driven world, it is vital for research to explore ways to improve the accessibility and usefulness of technical information to the public.
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This paper expands energy feedback research by introducing a novel system, community-scale energy feedback, with the potential to expand public participation in energy systems while helping strive toward low-carbon communities. Acknowledgements This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650044. 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. References [1]
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