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Data Issues and Promising Practices for Integrated Community Energy Mapping

Author: Jessica Webster, CanmetENERGY

Publication Date: March, 2015

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Municipalities, utilities, and the public can use energy mapping to make informed decisions on energy end use and renewable supply options in the built environment. Integrated community energy mapping (ICEM) is an emerging mapping and modelling approach that leverages existing and new datasets and available building and technology energy modelling software in combination with geographic information systems (GIS) to provide scalable spatial decision support to energy and emissions planning, policy, and program development, and their implementation and verification. Applications include energy and emissions inventories for municipalities, utility conservation demand management and demand-side management program planning and identification of smart energy network opportunities.

Between 2008 and 2012, Natural Resources Canada led and supported ICEM research projects. It was observed that many of these projects faced similar data challenges.

This report outlines municipal and utility user needs for energy mapping, providing the basis for a detailed investigation of common technical barriers and knowledge gaps in working with ICEM data inputs. The datasets required to map and model baseline and future energy, emissions, and costs scenarios for the housing and building stock are explored.

Two case studies describe collaborative and data issues: the Integrated Energy Mapping for Ontario Communities (IEMOC) project and the Spatial Community Energy Carbon and Cost Characterization (SCEC3) model for Prince George, BC. For each dataset and distinct data integration activity, specific issues are described. Themes that emerge include access, structure, level of geography, and consistency. Importantly, the protection of personal and commercially sensitive information is not an issue but rather a prerequisite to be addressed for datasets individually and when integrated.

The data issues encountered in energy mapping projects to date are typically larger than can be tackled by individual proponents on a project basis. They are of concern because they translate into quality issues that impact the reliability, replicability, accuracy, and cost effectiveness of energy mapping initiatives and, by extension, the policy, planning and programs being designed, implemented and monitored. This paper aims to identify and describe the data issues so they may be resolved systematically by organizations working collaboratively to implement promising practices to advance community energy planning and utility conservation and infrastructure planning.

A number of best and promising practices for ICEM were used successfully in the IEMOC and SCEC3 projects to respond to data issues; a third case study, the Tract and Neighbourhood Data Modelling (TaNDM) project, offers new methods for data integration and aggregation. The best and promising practices cover the themes of collaboration, access, consistency, structure, and level of geography. Guidance from these three projects is augmented in this discussion with information from NRCan’s Canadian Geospatial Data Infrastructure (CGDI).

Best organizational practices enabling data access for clearly defined purposes include commitment to collaboration and continuous improvement, conducting user needs assessments, developing use cases, defining scopes, and gathering data requirements. Data should be evaluated to determine sensitivities and shared to enable further research and development of authoritative and useful data products. Requirements around privacy and the commercial value of data must be respected and managed appropriately; privacy impact assessments, privacy protection principles, non-disclosure agreements, and data licenses are useful mechanisms.

Obtaining data closest to the source is another best practice that, although organizational in nature, will reduce project risk by accessing the most relevant and authoritative data. Seeking clarification on structural and consistency issues from data custodians is also recommended. Although not all datasets needed for energy mapping are yet accessible via open data, this best practice shows how governments can make administrative datasets more readily available.

Best practices to improve data consistency include developing authoritative parcel fabrics and civic addressing on a provincial basis, although this may be precluded in some jurisdictions for commercial reasons. Further best practice guidance is required on greenhouse gas emissions factors, capital costs, and the use of modelled energy data. All of these datasets and associated best practice guidance will provide a strong foundation for energy mapping when openly accessible in all jurisdictions.

Promising practices to improve consistency include assessing the data to determine its highest and best use for energy modelling and mapping, identifying standard building categories across collaborating organizations, and developing standard building information reports.

To tackle issues relating to level of geography, sharing data (under prescribed conditions as defined by non-disclosure agreements and/or data licenses) at the finest spatial resolution—at the level of the parcel, building, and energy meter — is recommended. Data integration at this scale is considered a promising practice as it enables the data integration to be done once; if maintained, this integrated dataset can serve multiple purposes. Linking all data to a unique numeric identifier, maintaining direct database/geodatabase linkages, and additional data tables to link building and unit attributes are promising practices for data matching, including for complex parcel-building-unit cases. Establishing a common method for municipalities to assign identification numbers and link parcel and building data for multi-unit residential buildings and other complex building types is also advised.

Data aggregation by building type or category to defined levels of geography and privacy thresholds are promising practices that generate robust energy and GHG emissions information by building type in a privacy-compliant manner. Energy use intensity and energy use per capita are key energy-related indicators that can be produced at various levels of geography through this approach.

In further ICEM research and development, to ensure the integrity and authoritativeness of data products as well as ensuring a positive stakeholder experience, it is important that quality assurance and quality control be performed at various stages in the ICEM development process. The Canadian Geospatial Data Infrastructure can provide numerous examples of best practices in other domains as well as data standards that can be leveraged by ICEM initiatives on a going-forward basis.

Table 1: Summary of data issues and promising practices by theme.
Theme Data Issue Promising Practices
  • Improper assumptions made about datasets originally collected and maintained for purposes other than energy mapping
  • Business models of organizations not designed to interact with each other
  • No prior business case for use of each other’s data
  • Collaboration and continuous improvement
  • Build a roundtable of data custodians and users
  • Engage broad range skill sets to assess data issues and means of their resolution (e.g., business strategy and policy, geomatics and IT, building energy, legal, etc.)
  • Hold workshops to build trust and to identify barriers, business needs, data requirements, and use cases
  • Use project management and business analysis best practices
  • Hold multi-stakeholder meetings to develop, promote common understanding of, and seek clarification on the methodology and data models under development
  • Users: Accessing data through ad hoc requests
  • Providers: Receiving multiple inconsistent data requests
  • Datasets for future modelling (e.g., future growth, utility rates) may not be accessible
  • Establish scope defining acceptable use cases
  • Assess level of sensitivity of datasets or data products
  • Develop standard reports
  • Elicit assumptions for future scenarios from those with local/domain knowledge  
  • Implement organization geomatics policies
  • Data gaps for specific attributes consistently, or individual records randomly 
  • Lack of complete civic address
  • Lack of clarity on modelling methods
  • Lack of standard assumptions for baseline emissions factors, energy prices, and capital costs
  • Identify potential causes of data gaps; develop methods to address limitations and approaches for filling data gaps
  • Develop authoritative civic address information
  • Use building and housing archetype modelling files according to provincial Building Code or National Energy Code for Buildings (NECB)
  • Provide guidance on standard assumptions for baseline and projected GHG emissions factors, energy prices and capital costs on a provincial, regional or community basis
  • Data originally collected and maintained for other purposes (e.g., property assessment)
  • Building types or categories defined differently by different organizations
  • Multiple parcel-building-unit configurations
  • Data maintained in different database types (e.g., relational or geodatabase) with different data linkages
  • Assess existing data to determine highest and best use for energy modelling and mapping purposes
  • Identify standard building categories across assessment authorities, utilities, municipalities, and NRCan
  • Link standard building categories, census tract and municipal identifiers to each building to enable aggregation
  • Link all data to a unique numeric identifier such as the parcel identifier (this may vary by jurisdiction)
  • Establish direct database linkages for simple cases (e.g., one parcel to one single-family unit) and maintain data tables to link building and unit attributes for more complex cases (e.g., multiple parcels to mixed-use unit)
  • Engage a third party to provide quality control, quality assurance review of data models, standard reports, new data products, etc.
Level  of Geography
  • Obtaining utility data at the individual building level
  • Significant undertaking of linking parcel-building-meter data
  • Aggregation of energy data by postal code
  • Establish data-sharing agreements
  • Integrate data at the parcel-building-energy meter scale
  • Build data relationships once and maintain them
  • Aggregate utility data by standard building category and levels of geography to privacy thresholds

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