Building Energy Mapping and Analytics: Concept Development Study Report

Characterize the state of development of energy mapping and analytics for building stock broadly; and

Inform and propose IT architectural practices and standards to enable mapping and analytics specifically of residential energy use and efficiency.

Utility perspective on conservation, demand side management, regulation is underrepresented in the responses.

Lack of awareness among stakeholders and building professionals of the potential for mapping and spatial data analytics to facilitate the transition to a low-carbon built environment.

Data access and sharing issues include availability, privacy, confidentiality, propriety.

Repetitive non-standardized methods are applied to collection, exchange, integration of datasets.

Data source methods and confidence are wide ranging and poorly documented, variously measured, modeled, inferred, estimated, assumed, etc.

Lack of access to retrofit cost estimates presents a barrier to deriving benefits from energy mapping and modeling data.

Lack of an overall data framework prevents connecting the scale and resolution of spatial data to particular use scenarios

It remains a challenge to connect archetyping methods (clustering / classification) with different use case scenarios

Data access technologies that account for privacy, confidentiality, anonymity, e.g. enclave processing, anonymization by aggregation + noise injection (differential privacy)

Adaptive classification and archetyping based on sample modeling

National systems for consistent energy data at multiple scales

Data sharing policies and standards organized to support critical use cases and stakeholders, e.g. federation contracts, mandated reporting.

National building data layer for comprehensive analysis of building types, energy performance, retrofit / upgrade technologies, costs, and benefits.

Community-utility cooperation facilitated by regional/national authority to understand opportunities /costs / benefits of new technologies and energy sources, e.g. renewables

Design of an extensible and standardized national building dataset "layer", leading to both national application and improved comparability of promising building energy analysis methods.

Sandbox activities such as interoperability pilots, modeling the mutual benefits of information sharing and data interoperability

Prototypes for an Energy SDI, demonstrating common availability of such technologies as cloud-based energy modeling, model-driven building archetypes, and enclave protocols for addressing data privacy and propriety constraints.

Development of energy poverty indices that take into account fine-scale socio-economic, climate, and geographic factors in assessing the impacts and mitigation of building energy costs.

OGC® Web Services Common Standard, OGC 06-121r9, , 2006

OpenGIS® Implementation Standard for Geographic information - Simple feature access - Part 2: SQL option, 2010

OGC® Land and Infrastructure Conceptual Model Standard (LandInfra), 2015

OGC City Geography Markup Language (CityGML) Encoding Standard, 2012

ISO 42010 standard: Systems and software engineering — Architecture description, 2011

Section 6 covers the background of the BEMA CDS, as well as previous and related work on Building energy

Section 7 details the goals, use cases, questions, and logistics of the BEMA RFI

Section 8 compiles and analyzes the RFI responses.

Section 9 presents the structure, discussions, and outcomes of public validation workshops building on what was learned from the RFI responses.

Section 10 discusses the challenges and opportunities gleaned from the RFI and workshop outcomes

Section 11 outlines what an energy spatial data infrastructure might look like that is able to support multi-scale building energy mapping and analysis

Annex A contains details of the RFI response

Utility perspective on conservation, demand side management, regulation is underrepresented in the responses.

Data access and sharing issues: availability, privacy, confidentiality, propriety

Repetitive non-standardized collection, exchange, integration of datasets.

Diverse data source methods and confidence, e.g., measured, modelled, inferred, estimated, assumed.

Lack of access to retrofit cost estimates are a barrier to deriving benefits from energy mapping and modeling data.

Lack of overall framework that connects scale and resolution of spatial data to particular use scenarios

Challenge to connect archetyping methods (clustering / classification) with different use case scenarios

Use cases (i.e. specific applications) cited by respondents significantly overlap all three (community, commercial, and governmental) scenarios

Use cases often involve the same stakeholders, but different objectives or priorities, e.g., individual energy decisions vs governmental policies and initiatives

Many different roles for building energy data:

Livable buildings (inhabitable, affordable, sustainable, protective) are essential to personal, family, community welfare. Livable buildings depend on energy and energy affordability

Buildings are also a substantial component of energy demand and mix (and increasingly — infrastructure). Energy demand and mix affect the environment and economy, are affected by policy, regulation, and markets at regional, subnational, national, and international levels.

Building energy decarbonization is significant in climate response, needs to balance livability and resource availability

Enterprise - stakeholders, scenarios, business cases, benefits and drivers, risks

Conceptual - information models and computational designs

Implementation - engineering solutions, platform and technology choices, deployment, lifecycle, human - system interactions

Review of Materials supplied by CanmetENERGY-Ottawa – including background documents on CEE Map, IEA Annex 70, and draft Request for Information (RFI).

Preparation of questions and question categories for the RFI

Identification of potential respondents

Outreach to potential respondents, and larger QUEST network

Assistance with preparation, promotion, facilitation for the OGC Energy and Utilities Working Group sessions and two Validation Webinars

Review of early results with project team

Project team meetings throughout

Interim Report, and this Final Summary Report.

Stakeholders

Applications and IT architecture

Data and governance

Requirements

Usage scenarios/use cases

Technology and techniques

Other factors

Energy Utilities

Energy Efficiency Agencies

Municipalities and Municipal Agencies (e.g. FCM)

Technical Institutes and Universities

Non-profit organizations

Software Providers

Government Agencies

Direct Email to Respondents identified (reach: 30)

OGC Energy & Utilities DWG list-serve (reach: 90+)

QUEST Newsletters (Atlantic, Central, Western. Reach: 5000+)

QUEST Social Media (on Twitter and LinkedIn)

A brief introduction to OGC (www.opengeospatial.org)

A recap of key findings from two Energy Summits (summarized below in Section 3.1)

Introduction to CanmetENERGY, CEEMap, energy mapping in Canada, and BEMA-CDS

Overview of Initial Findings from the RFI (presented by OGC, and summarized in Section 3.2)

Lightning Talks:

Solving pain-points

Energy cost reduction

Energy/peak demand reduction

Climate change and CO² (reduction/management)

Acceleration of clean energy deployment / cost reduction

Infrastructure Renewal / Cost Avoidance

To Inform Policy

To Improve Customer Choice

To Improve Utility Planning / Programs

Integration of building attribute and energy data (estimated, modeled, or measured) at parcel level, to support building energy modeling and wide area analysis.

Community Energy Planning– Calculate and visualize baseline and forecast energy use and GHG emissions across a community, to target investments in efficiency and clean energy integration, and to provide users (e.g. a utility, community stakeholders) a tool for measuring impact of investment decisions.

Integration of Green Button energy use data and spatial representation of energy use data, to create more accurate models of consumption to help determine what efficiency improvements and demand response (DR) programs are suitable/available to a geographic location and energy consumption profile.

Renewable resource assessments (yield estimation) and integration with demand profiles

Model what-if scenarios/ impacts of technologies and policy decisions on the metered building stock

Improve accuracy of models (historical and forecast scenarios), using OGC standards and statistical methods to compare modeled vs measured data

Privacy protection: de-identification, processing, normalization, aggregation, and visualization of energy demand (across all fuel/building types) to an appropriate scale and privacy threshold.

Smart Cities / Smart Energy Communities– emerging requirements

Distribution grid model data management: for example, OGC standards could be used to create a high quality Geographic Information System (GIS) model of an energy network for applications across a utility enterprise (e.g. for asset management; integration of DER; automation/ADR; outage management etc).

Capacity constraint and network analysis, power generation capacity/resources assessment and pre-certification (demonstrated with OGC CityGML Utility ADE) to enable Distributed Energy Resources (DER) analysis / site selection.

*Probabilistic forecasting *using weather data elements provided through The Weather Information Exchange Model - WXXM and using IEC CIM – for probabilistic forecasting, outage preparation & response/recovery, peak demand reduction, Volt/Var control, intermittent renewable resource integration, etc.

Outage Management – Data Integration, Distribution Network awareness, and Exchange.

Improve Customer Information Management (CIM)/Distributed Energy Resources (DER) for load balancing and power quality. Focus: how OGC standards can improve interoperability of distributed energy resource management system (DERMS) and GIS.

EV Charging Network Management – has been demonstrated with WFS-T

Augmented Reality for Inspection/Surveying for utility infrastructure, community energy projects

Integration of all fuels data for improved supply management and community energy demand profiles

Develop standards for integrating underground utilities e.g. OGC landInfra/InfraML or PipelineML

Oil & Gas pipeline– some unique and common requirements with electric distribution networks

Oil Spill Response (already demonstrated, OGC)

Demonstrate how OGC standards (e.g. WMS, WFS, WPS, OGC APIs) can support energy data exchange for building level and wide area mapping and analytics, including backcasting and forecast scenarios (i.e. high-efficiency vs business as usual), while protecting privacy.

Improve accuracy of models (historical and forecast scenarios), using OGC standards (WMS, WFS, WPS, etc) and statistical methods – by comparing modelled vs measured data.

Better integrate energy and building data for demand profiles, for example through creating an Energy XML Schema, or Relational Schema, Hierarchical Schema, Semantic network requirements, that can underpin building energy mapping and analytics using OGC standards-based applications. This would ensure the ability to match building archetypes and utility customer classes, as well as aggregate data to different scales appropriately.

Demonstrate how CityGML or IndoorGML standards can handle necessary energy related attributes for building modelling, such as space type, mechanical systems, insulation levels. Also, advance CityGML demonstrations to integrate metered utility data.

Demonstrate how OGC standards (e.g. OGC WPS) are used for de-identification, processing, normalization, aggregation, and visualization of energy demand (across all fuel/building types) to an appropriate scale and privacy threshold.

Demonstrate how to integrate renewable resource information, for example as OGC WCS (coverages) with CityGML (Utility ADE) capabilities for yield estimation and modeling.

Demonstrate how OGC standards can be used to create high quality GIS model of energy network for applications across a utility enterprise, including for capacity constraint analysis, probabilistic forecasting, outage management, load balancing, power quality, EV network management, surveying/inspection, etc.

For implementation of OGC WPS, there’s a need for specific profiles and improved ability for handling parameters (user defined parameters etc) and for time-series representation.

Access to Data – non standardized energy end-use data.

Integrating modeled building energy performance with aggregated/normalized energy consumption (metered) data.

Privacy protection, legal framework, exchange protocols, data standards / inconsistencies, risk perception.

Energy Mapping for municipalities – on-demand provision of complete energy, GHG, and efficiency maps (historical, seasonal, and forecast /what if), below the municipal boundary scale.

Distribution Network Model – developing a complete / connected model, integrating location-based elements to improve forecasts and ‘smart DER/DR assets’, leveraging the model for OT and IT applications

Lack of understanding of ROI, utilities not fully leveraging standards.

Access to Best Practices (albeit growing)

Select scenarios for further research, which focus on achieving key requirements / providing the greatest impact across value streams.

To advance solutions, OGC Members can contribute Concept Development Studies (such as BEMA-CDS), Profiles of Implementation, develop Engineering Reports, and collaborate to demonstrate interoperable solutions for key pain points in the energy and utility domain. Members could consider collaborating on interoperability pilots involving: OGC standards, with IEC CIM, DERMS, BIM, Green Button etc., for specific scenarios listed above, or as part of national SDI. The OGC E&U DWG can enable coordination between relevant Standards Working Groups, to facilitate development of interoperability pilots / projects, where new combination of standards are needed to solve pain-points in Energy & Utilities Domain

Build trust and prove business case/value proposition:

Develop best practices or standards for energy use data exchange, integration, and visualization. Publish outcomes.

Municipalities, utilities and building owners collaborate to model all energy consumption in municipalities.

Identification of district energy potential, retrofit hot spots, validation of city-wide energy and GHG emissions.

Demographic energy analysis e.g. energy poverty

Enabling communities with an additional tool to understand their energy resources and make investment decisions in renewable energy projects.

Renewable energy system design and operation, design of storage systems, transportation energy use mapping

Grid integration of renewable energy

Municipalities, utilities and building owners collaborate to model all energy consumption in municipalities.

Individual building owner energy cost management

Sensor-based analysis - how can these technologies can be supported in the context of building energy.

Integrating supply and demand side mapping to identify regions that might be net-electricity producers (higher potential supply than demand) and therefore help to map flows of electricity in a given area.

Market Opportunity Assessment (for specific technologies, programs, services)

Energy rating reports (building labelling), digital audits and labels, assessing energy ratings averages and trends at different levels (nation, region, location, building)

Demographic energy analysis

Sensor-based analysis - how can these technologies can be supported in the context of building energy.

Building energy mapping tool and analytics for building capacity in research areas to support innovation

Cross-Domain Analysis – additional value when integrated with other domains (e.g. climate change, local development, population health, etc.)

Building energy modelling compliance within design applications.

EnerGuide Rating System (An EnerGuide rating is a standard measure of a home’s energy performance)

Partners for Climate Protection (PCP) protocol for Greenhouse Gas Inventories

OGC CityGML +/- Energy ADE (CityGML is an open data model and XML-based format for the storage and exchange of virtual 3D city models. The CityGML Energy ADE extends the CityGML Standard by features and properties, which are necessary to perform an energy simulation and to store the corresponding results.)

OGC IndoorGML (specifies an open data model and XML schema for indoor spatial information).

buidingSMART Information Delivery Manual (The Information Delivery Manual (IDM) aims to provide the integrated reference for process and data required by BIM by identifying the discrete processes undertaken within building construction, the information required for their execution and the results of that activity)

OGC GeoSPARQL (The OGC GeoSPARQL standard supports representing and querying geospatial data on the Semantic Web)

PROV-O (PROV-O is a lightweight ontology that can be adopted in a wide range of applications)

ifcOWL (ifcOWL provides a Web Ontology Language (OWL) representation of the Industry Foundation Classes (IFC) schema. IFC is the open standard for representing building and construction data)

gbXML (An industry supported standard for storing and sharing building properties between 3D Architectural and Engineering Analysis Software.)

Varies from 5 minutes to 1 year

Yes – 14, No – 6, No response – 13

Building data such as footprint, height, type, construction year, window-to-wall ratio, HVAC type, heating and cooling loads, floor area, envelope materials, cost, location, & other fields

Energy end-use data (all fuels), demand and peak data (where available), & carbon intensity

Supply/Demand data on propane and heating oil at the community level

Photovoltaic electricity generation

Renewable Energy Resource data (e.g. solar irradiance, etc)

Modelled energy consumption and EnerGuide audits by building archetypes, age, size,

Billing or Metered data, when applicable

Weather data (temperature, humidity, wind speed)

Occupant socio-demographics

Building Codes

Other

No archetypes - model each individual house

3 archetypes: residential, commercial, industrial + energy usage

4-10 archetypes clusters defined by an unsupervised machine learning algorithm that clusters dwellings by age, floor area, location, energy and carbon footprint, ranging from

30 residential + 50 non-residential

5 building types subdivided in 10 age classes in German IWU archetype library

Many different housing typologies referred to in Official/Community Plan policies and zoning by-laws, as well as building codes

97 building type and vintage combinations modelled in OpenStudio

FSA (geographic unit) aggregates

768 comprised of 16 Building Types for 16 Climate Regions for three construction periods found in Commercial Buildings Energy Consumption Survey (CBECS)

~6500 existing housing archetypes across Canada modelled in Housing Technology Assessment Platform (HTAP)

Use cases often involve the same stakeholders, but with different objectives or priorities, e.g. individual energy decisions vs governmental policies and initiatives. It will be important to understand their particular requirements for energy data exchange and BEMA.

Limited response from utilities, so utility perspective on conservation, demand side management, regulation is underrepresented. This requires further study (see section 4.0)

Significant activities that involve repeated collection, exchange, integration of datasets with very little use of standards. A national CEE Map and/or energy SDI could reduce duplication.

Diverse data source methods and confidence, e.g. measured, modelled, inferred, estimated, assumed. A national CEE Map and/or energy SDI could improve confidence.

Energy data for inventories and models are derived from different sources and methods including:

Disparate area +/- time interval units for different datasets limit granularity of aggregation. Aggregations are more accurate for noisy data, less accurate for biased data. Unclear what scale is appropriate for a given public dataset or policy. Lack of overall framework that connects scale and resolution of required spatial data to particular use scenarios. Solutions should enable scaling of data beyond a privacy threshold, for various use cases.

Various techniques are applied inconsistently by different organizations to estimate current and projected end-use and efficiency opportunities. Best Practices or Standards could address this gap.

Downscaling of national/provincial values using spreadsheets is not uncommon

Data access issues: availability, privacy, confidentiality, propriety. Need Common protocol for data disclosure and anonymity. Sharing of non-aggregated microdata may be limited by privacy and/or proprietary concerns, complicating production of aggregate measures.

Lack of access to relevant data on estimated cost and effectiveness of retrofits identified as a particular barrier to benefiting from energy mapping and modeling data.

Lack of access to relevant data on availability and potential of various energy types

Lack of attention to issues of energy poverty in programs that target energy conservation and carbon reduction.

The strategic advantages of geospatial analysis are not broadly understood by many actors

To harmonize observed overlap between scenarios (community, commercial, and governmental). This would mean services in CEE Map or Energy Spatial Data Infrastructure may serve multiple value streams / provide co-benefits at reduced cost.

Opportunity to connect archetype delineation (e.g. clustering / classification) with application usage (whether computational, comparability, policy)

Alignment of building archetypes and classifications with energy datasets and each other

Alignment of geographic (climate, policy, regulatory, administrative, market area, generation, demand) units

Metadata for data definition, quality, provenance, currency

Exchange models / formats for energy usage / demand / cost data

Exchange model / format for building retrofit design and cost data

Commercial vs personal vs governmental data

Introduction to CanmetENERGY, CEEMap, and BEMA-CDS (presented by NRCan)

Overview of Findings and Analysis (presented by OGC)

Q&A and Discussion

Data access that accounts for privacy, confidentiality, anonymity, e.g. enclave processing, anonymization by aggregation + noise injection. Could also be achieved through private Spatial Data Infrastructure (SDI).

Adaptive classification and archetyping based on sample modeling

National systems for consistent energy data at multiple scales

Data sharing policies and standards organized to support critical use cases and stakeholders, e.g. federation contracts, mandated reporting.

National building data layer for comprehensive analysis of building types, energy performance, retrofit / upgrade technologies, costs, and benefits.

Community-utility cooperation facilitated by regional/national authority to understand opportunities /costs / benefits of new technologies and energy sources, e.g. renewables

Register & Catalog (CSW, API Records)

Search (CSW, OGC API xxx)

Access (WFS, WCS, STAPI, API Features, etc.)

Process (WPS, API Processes)

Sample (API EDR - DAPA)

Transact (WFS, API xxx transactions)

Authenticate - Authorize (OpenID, HTTP Auth, Federated DCS, etc.)

Link - Relate (KB, GeoSPARQL, GQL, JSON-LD)

Building layers:

Local

Local / Regional

National

Multi-scale

Technical Challenges:

Data Issues

Public-private partnership (for data collection, integration, aggregation etc)

Interoperability pilot / project, standards work

Phased development of eSDI - private vs public services

Modernization of decision support systems

Providing input to the Canadian Centre for Energy Information consultations

Conducting a utility focused study or national survey **(as outlined in section 5) - to understand utility perspectives, information systems, challenges and opportunities

Creating the National Building Layer to enable comprehensive analysis of building types, energy performance, retrofit / upgrade technologies, costs, and benefits.

Developing *authoritative data *and making it accessible on an open or licensed basis - Could be achieved through improved access to and interoperability between applications, as well as *data sharing policies and standards *organized to support critical use cases and stakeholders, e.g. federation contracts, mandated reporting.

Leveraging existing and developing new* technical guidance *on highest and best use of data from different sources and types (e.g. measured, modelled, inferred, estimated, assumed), E.g. Canadian Standards Association Quality Assurance and Quality Control of Building Energy Modelling for Program Administrators

Providing guidance for and adopting standards (including OGC standards) for energy data exchange and mapping, while addressing data access issues: availability, privacy, confidentiality, propriety._ This should include guidance on how to use specific OGC standards in relation to specific use cases, and_ common protocol for data disclosure and anonymity.

Establishing* best practices for building energy mapping and analytics, building archetype classification/analysis, and wide area representation, of measured (e.g. metered consumption data, building type), modelled (e.g. HTAP/BTAP), inferred (e.g. GHG emissions) and estimated data types. Furthermore, *defining archetyping methods (clustering / classification) for different use case scenarios and stakeholder type/capacity. A CEE Map should allow for multiple types of archetype analysis / different granularities, facilitate alignment of building archetypes and classifications with energy datasets and each other.

Creating a *multi-disciplinary team *for advancing CEE Map and/or establishing and Energy SDI capable of reducing duplication of collection, exchange, integration, and visualization of datasets, using OGC Standards, for various use cases, scales, time scales, etc, while respecting privacy and confidentiality. Create an overall framework which connects use case scenarios, to spatial and temporal scale and resolution of data.

CEE Map or energy SDI could provide access to* various national data layers* (e.g. NBL) as well as energy data (modelled, measured, etc), as well as data necessary for supporting use cases. While not all suggested data types might be feasible to provide on a national basis, they are relevant to the scenarios described above:

Establishing a system for enclave processing, anonymization by aggregation + noise injection. Could also be achieved through private Spatial Data Infrastructure (SDI).

In order to create an energy SDI and fully interoperable CEE Map, there is need for undertaking *Interoperability Demonstration **of OGC standards *to meet the requirements of CEE Map, integration with HTAP/BTAP, CGDI, and Distributed Data Sources. This can also help inform OGC Standards Development, for example:

Engaging post-secondary education and private firms, in developing Energy SDI, as needed.

Developing* public education or communication strategy* to promote the benefit of spatial data infrastructure and energy data to all Canadians.

Ensuring Metadata for data definition, quality, provenance, currency

Update graphic templates (see Figures 1 and 2 in the Appendix) to include newer standards and APIs. Update Figure 3 to capture the specific use cases that CEE Map or an Energy SDI may support, and align with OGC and other standards.

Energy consumption figures in Therms, KWh, Megawatts, GJs, or BTUs, can be used to describe, compile/add, compare the total consumption for an area (e.g. kWh by parcel, hectare/ha, kilometre/km²), developable land (e.g. GJ/ha), building type and archetype (e.g. GJ by footage - metre/m²), total floor space of a project or job (e.g. m²/job), customer class (e.g. low-rise residential, commercial, general service <50kW or >50kW) or end-use type, and assess potential alternatives. This can include measured electricity and gas consumption baseline data (archival, published), modeled usage / forecast loads / per-capita consumption, real-time (every 15 minutes, hour, day, month) as well as forecast values (seasonal / annual / long-term), usually averaged by building archetype (and matched to parcels) or summed by zone, neighborhood / district, community or region). Anomalies from standard archetype energy consumption profiles can help users compare their usage (whether higher or lower) to average rates. Estimates based on building archetypes, aggregation by geography, normalization by population, and comparison to actual data (heat maps depicting deviations/anomalies from statistical average), are methods to avoid personal identification / problems related to privacy of data and re-identification.

Current monetary value of fuels ($ per unit consumed) is important in assessing total funds expended per-capita, as well as Internal Rate of Return (IRR) and total potential savings / Return on Investment from investments in efficiency, or adoption of alternative supply options to meet space heating and power requirements. What-if scenarios (e.g. switching fuel type) can be evaluated through comparison of monetary values and units consumed

For renewable energy technologies, the installation cost per unit energy generated ($/GJ), the operating cost per unit energy generated ($/GJ/yr), the CO2 emissions per unit energy generated (kg/GJ), Maximum applicability to the built form (%), Maximum applicability to displace conventional building energy generation (%), revenues from sale of energy including feed in tariffs (where applicable) – ($/yr).

Current monetary value of alternatives (by unit generated / offset), to help determine resource capacity, as well as Internal Rate of Return (IRR) and Return on Investment (ROI) of developing renewables (wind, solar, tidal, biomass), and participating in programs (e.g. DSM/Efficiency, Demand Response, net-metering, embedded generation, combined heat and power, district energy/heating). What-if scenarios (e.g. supply type selection) can be evaluated through comparison of monetary values and units generated / offset from traditional sources.

Emissions co-efficients (CO~2 ~/ kWh for electricity, m³ for gas, litre for gasoline) are used to convert energy consumption data into CO~2 ~emissions per lowest unit of comparison, based on the supply mix in each Province/Territory, and to model emissions from various energy supply types, baseline and future community energy scenarios. This can be used to compare the resulting emissions from selection of community energy options (such as district heating, CHP, and community energy systems).

Linking building types, categories, attribute codes, customer classes, energy types, common IDs.

Building Types, Archetypes, Customer Class Relations

Mapping energy intensities comparably across geometric areas requires matching of building typologies with building archetypes with utility customer classes, along with identification of common data links (e.g. name, type, class, ID), Building Type / Class and Archetype Attributes (e.g. stock age/vintage, size, height, orientation, footage, and other energy profile attributes). For example, Building Archetypes are ‘mapped’ to electricity and gas customer classes.

Other data sources for spatial analysis can include vacant land-use inventory, present and proposed zoning, annexation plans, census data projections, regional planning studies, utility customer-usage information, and load research. These disparate data sources can translate future population densities into load projections and give greater insight into customer behaviour. With this data, utilities can organize sections of projected load growth into polygons as separate layers within their enterprise GIS (Connors, 2011).

Occupant Characteristics / Consumer Profiles: though useful, this may not always be available, and is not collected in a standardized way. Surveys and targeted interviews can identify energy consumer characteristics, but must be constructed in a non-intrusive manner and with regard to privacy protocols.

Efficiency Program Participants: identification of existing efficiency program participants, successful retrofits, and conservation subdivision designs, may be useful for knowledge exchange for before and after retrofit comparison. If compared with average consumption rates, baseline and forecast scenarios may be adjusted to reflect local circumstances and to compare efficiency retrofits impact (on costs, emissions).

Actual Electricity / Gas Consumption / Billing Data for individual consumers (unless volunteered / consent obtained)

Personal Data (e.g. name, occupation, gender, age, etc)

Specific End User Profiles and Project Deployments (unless volunteered / consent obtained)

Federal/Provincial/State Governments – Open Data / Geospatial Portals

Utilities (public/Crowne, or private)

Green Button

Statistics Agencies

Tax Authorities

Building Owners

Municipalities

Planning Commissions

IoT / Sensors

EO data providers

Data warehouses

Global Atlas (e.g. IRENA), Web Service providers, or Brokerages