2 National Renewable Energy Laboratory Golden, CO, USA
3 EXPERIMENTAL RESULTS
Once all physical, demographic, and energy-related datasets were integrated into ArcGIS, filtering analysis was applied using all the data layers to provide insights into existing energy use issues and opportunities for energy efficient strategies to reduce energy use during peak demand hours.
Preliminary studies utilized the selective filtering of real-world and simulated energy use datasets in ArcGIS to examine factors that could inform strategies related to building retrofits, and load shifting through changes in energy use behavior.
3.1 Building Retrofits
The first phase of analysis sought to identify which, if any, buildings had potential for building envelope and equipment retrofits. Data used for analysis included building type, building age, income level, and simulated energy use data through the UBEM. Buildings were grouped into age ranges for every 25 years then mapped in ArcGIS accordingly.
Filtering analysis was used to visualize the relationship between specific age ranges and energy use. Visualizations of each age range illustrated which of these buildings were consumers of high energy based on occupancy, size, and simulated energy use (Figure 3). Furthermore, this multi-dimensional visualization allows us to identify the relationship between users’ energy use intensity and building types, occupancy level, building size and income. The different layers that are used for our analysis are illustrated in FIGURE 3. Visualizations also showed that the majority of the offices and residential buildings in this area were built prior to 1950, indicating the likelihood that these buildings have inefficient envelopes, heating, and cooling equipment.
The analysis provided several insights as to which subset of aged buildings with high energy consumption might require different energy efficient strategies. It also suggested that focusing on energy efficient strategies for aged buildings could have a greater impact on reducing the overall energy use of the neighborhood than if applied to newer buildings.
Figure 3. False color simulated hourly energy use for buildings in the Syracuse downtown neighborhood. Users can select a building and view a GUI that presents detailed information about the selected building, including age, types with their dominating energy use schedules,
and total energy use over a typical 24-hour period.
Layered onto the analysis was demographic data related to income, to factor in potential barriers to building retrofit solutions such as upgrading the building envelopes or equipment. A majority of aged buildings with high energy consumption predictions were located in areas with lower income, raising concerns as to the economic feasibility of such upgrades and pointing to the investigation of alternative strategies. In this situation, behavioral adjustments presented a possible alternative approach that could be effective in stabilizing the energy demand during peak demand hours.
This would require shifting heavy duty equipment use to evening hours and applying pre-heating strategies.
3.2 Energy Load Shifting
The next phase of analysis identified aged buildings that were likely to contribute to high energy use specifically during peak demand hours, in order to implement possible load shifting strategies to offset grid pressures. This analysis layered geospatial data for building type, hourly occupancy, and simulated hourly energy use data. The spatiotemporal visualizations illustrated that peak energy demand for commercial and office buildings built prior to 1950 surpassed the grid energy supply during late afternoon hours.
During this high demand period, the city is obliged to depend on peaker plants, which typically are expensive to operate and use more fossil fuels than non-peaker plants [3]. To avoid use of peaker plants, load shifting strategies were proposed to analyze their potential impacts on overall building energy use for the downtown neighborhood.
Strategies included energy use behavioral adjustment by shifting heavy-duty equipment use to evening or early morning hours, when energy demand is lower. Load-shifting was also applied by implementing heating and pre-cooling strategies. In this case, HVAC systems schedules and setpoints were modified to operate at a higher level in hours that preceded the afternoon peaks; this would require building inhabitants to turn-off the HVAC systems or set an energy efficient setpoint during the peak hours while maintaining comfort levels.
In order to test and visualize the effect of the proposed load-shifting strategies, behavioral inputs of our baseline UBEM were modified to reflect these updated behaviors. We modified the operational schedule inputs of HVAC equipment, and modified the intensity inputs such as heating/cooling setpoints to reflect the proposed strategies.
Schedules of energy use in the UBEM represent a percentage of energy use for each hour of the day. New simulations were generated and their outputs updated directly to the ArcGIS hourly geodatabase for building energy use features. These results were then compared with the existing conditions in an interactive GIS interface (Figure 4).
Figure 4. Example of interactive slider viewer comparing energy use before and after load shifting strategies are applied.
4 DISCUSSION
4.1 Summary of Findings
Through the use of ArcGIS as a primary interface for analyses, we were able to visualize the peak energy use demands spatiotemporally by layering together simulated energy use data, land use, building type, building age, and occupancy. Patterns of energy use for specific building types and ages were easily filtered and isolated for spatiotemporal analysis, which enabled a more holistic understanding of the potential feasibility, as well as possible barriers, to implementing strategies such as retrofits or load shifting.
These preliminary analyses are modest examples of what could be further studied for the future of urban energy systems. Many other physical, environmental, and socioeconomic factors are important to consider in the evaluation of scenarios and strategies. Layering this information might reveal unexpected concerns but also potential solutions.
The workflow presented creates a useful feedback loop between UBEM and ArcGIS, making the iterative testing of urban energy strategies in the context of other data more easily achieved. When the UBEM gets modified and results updated, the data is easily integrated to the ArcGIS building objects and attributes. This process of integrating simulation results and GIS is automated, so that changes made to an UBEM can be immediately simulated and results viewed interactively in ArcGIS. The feedback loop works in both directions; the UBEM produces data for ArcGIS visualization and analysis, and the layered information coming out of GIS can produce important insights and new parameters that inform the UBEM development.
4.2 Opportunities for Visual Analysis and Dissemination In contrast to existing methods for visualizing outputs from UBEMs, which tend to be static 2D or 3D views, our approach enables the user to spatialize the building energy use data layered with other relevant information. The spatiotemporal visualization of multidimensional datasets
through an interactive GIS platform facilitates analyses of energy efficient strategies, not in the black box of an energy model, but in the open-ended context of real-world data. In our process, interactive visualization in ArcGIS is the primary method for exploring datasets, interpreting and investigating analysis results, and communicating valuable findings. Since each data layer can include attributes with high granularity, the user can visually explore and manipulate the data at different levels. Data is spatially located and can be identified using dynamic selection, which can also be used to filter data sets for analysis; by selecting specific layers, the user can isolate and compare layers of data/outputs. Within the ArcGIS platform users can also create dashboard charts which are dynamically connected to the spatial data. This aspect allows users to run hot spot analysis by selecting specific results in the dashboard charts that link the user to specific areas in the map. Datasets with temporal components can be visualized spatially though sliders and in a dashboard through line charts. To spatially visualize time series data, data should be summarized in time step layers and linked to a timeline slider. The user can also visualize temporal spatial-data overlaid with other data layers. This temporal visualization can be used to identify anomalies in behaviors and patterns.
Custom generated data layers can be exported to a GUI, where the layout can be edited, and some visualization features can be added to make the interface user friendly.
This interface can be then published and shared with various stakeholders (urban planners, utility companies, policy makers, etc.) to facilitate evaluation and communication of sustainable urban planning and resource management strategies.
4.3 Limitations and Future Work
As with any creation and use of data, there are limitations to the accuracy and assumptions behind the data, including that which is simulated. Understanding the bias behind data and how data can be manipulated are also important considerations when maps are generated for communicating a position or set of strategies. In our examples, there are conclusions drawn between income level and economic feasibility, or between the outdated conditions of HVAC systems and older buildings that would require further research to verify the state of individual buildings and their systems. However, the workflow is not intended to solve problems directly, but rather assist with clarifying questions, and identifying issues and opportunities that could benefit from further investigation.
Future work focuses on implementing this process as a plugin to UBEMs for a user-friendly exploration, analysis, and visualization interface. Further development of a GUI could be action-based to enable users to easily explore a library of energy efficient strategies, generate input for an UBEM, and obtain updated results through the automated process for quick viewing in the context of other socioeconomic information. Incorporating benchmarking
data as a GIS layer would also be beneficial for calibrating the UBEM and for evaluating strategies in the context of city-wide policies for building energy use. With 24 U.S.
cities with benchmarking policies in place, workflows with GIS interfaces such as this one could address challenges related to communicating the complex data to those outside the building energy efficiency community. Finally, testing the workflow and interface with stakeholders will provide valuable feedback for improving its usability and application in real-world settings and communities. This is especially important for enabling those who live in disadvantaged neighborhoods to articulate how their own communities might be understood by the interface, in light of investment and development of sustainable strategies.
5 CONCLUSION
This paper described a workflow to integrate simulated building energy consumption data associated with variable energy efficiency scenarios within a GIS platform for interactive spatiotemporal visualization to support energy efficient strategies and climate adaptation decision-making.
A feedback loop was developed to link UBEM hourly simulation results to building attributes in ArcGIS for visualization and analysis of energy efficient strategies over time and in the context of other georeferenced data. This work addresses current challenges with UBEMs by developing an automated process to integrate simulated building energy performance data layered with GIS, enabling users to selectively view, analyze, and update composite data layers for a more holistic understanding of the potential feasibility, as well as possible barriers, to implementing strategies. Analysis of a case study of downtown Syracuse, New York, demonstrated how the workflow provided insight into existing energy use issues and preliminary evaluation of load-shifting strategies. Future work points toward implementing this process as a plugin to UBEMs, further development of the GUI, and testing for stakeholder input.
ACKNOWLEDGMENTS
This work is funded in part by the Syracuse University School of Architecture and the Syracuse Center of Excellence Faculty Fellows program. The authors would like to thank student collaborators Katharina Koerber, Chenxie Li, and Harshita Kataria for their contributions to the research.
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