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Helpful Places

Government of Canada – DTPR AI Register

AI-Assisted Energy and Emissions Optimization for Industry

Energy Efficiency · Ecology

What it collects

Operational data
Anonymized data
  • Industrial process data from client facilities — measurements, operational parameters, and performance records from manufacturing or production systems. Industries connect EXPLORE locally to their own databases. Linkage with iEMIS (industrial energy management information system) has also been developed and demonstrated.
About a measurement
Anonymized data
  • Sensor and instrument readings from industrial processes — energy consumption metrics, emissions levels, temperature, pressure, flow rates, and other physical measurements used as model inputs.
Run by
Natural Resources Canada (NRCan)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

Energy EfficiencyEcology

EXPLORE is a machine learning software tool developed by Natural Resources Canada that helps industrial facilities model their processes, predict outcomes, and optimize operations to reduce energy use and greenhouse gas emissions. Industries run the tool locally on their own data, connecting to their databases to clean data, build predictive models, and monitor key performance indicators. The tool is used by both government employees and industry partners, and operates on industrial process data rather than personal information about members of the public.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Industrial process data from client facilities — measurements, operational parameters, and performance records from manufacturing or production systems. Industries connect EXPLORE locally to their own databases. Linkage with iEMIS (industrial energy management information system) has also been developed and demonstrated.
About a measurement
Anonymized data
  • Sensor and instrument readings from industrial processes — energy consumption metrics, emissions levels, temperature, pressure, flow rates, and other physical measurements used as model inputs.

Processing

Classification & Prediction
  • EXPLORE builds descriptive and predictive machine learning models of industrial processes to forecast outcomes such as energy consumption and emissions levels under varying operational conditions.
Optimization
  • EXPLORE includes optimization capabilities that identify operational settings minimizing energy use and GHG emissions while satisfying process constraints.

What it does

Deciding (Analytical AI)
Human decides
  • EXPLORE develops predictive and descriptive models of industrial processes, producing scores, predictions, and KPI rankings that inform operator decisions about process adjustments. Human operators review and act on model outputs.
Sensing (Perceptive AI)
Human decides
  • EXPLORE connects to industrial databases and performs semi-automatic data cleaning, transforming raw process data into structured inputs suitable for modelling.

Outputs

A recommendation or prediction
Anonymized data
  • EXPLORE produces recommendations and predictions about optimal process settings, energy savings opportunities, and KPI forecasts. These are advisory outputs reviewed by human operators and engineers, not binding automated decisions.
About a measurement
Anonymized data
  • EXPLORE produces KPI dashboards and model outputs representing predicted or monitored measurements — energy consumption forecasts, GHG emission estimates, and process performance indicators — at the facility or process level.

Run by

Natural Resources Canada (NRCan)
  • Natural Resources Canada developed and deploys EXPLORE to help Canadian industries reduce energy use and GHG emissions through machine learning-based process modelling and optimization.

Government of Canada AI Register — EXPLORE

Built by

Government of Canada
  • The Government of Canada developed EXPLORE internally; it is not a commercially licensed third-party product.

Government of Canada AI Register — EXPLORE

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Natural Resources Canada has access to the EXPLORE tool and its outputs as the developing department. Industrial clients run EXPLORE locally on their own data, meaning process data does not leave their premises.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyEXPLORE is listed on the Government of Canada's public AI register, providing transparency about its purpose, capabilities, and deployment. Industrial users of the tool have visibility into the modelling logic as the tool operates locally on their own data. Members of the public can consult the register entry for information about how the system works.

Risks and safeguards

  • Environmental harmIf EXPLORE models produce inaccurate predictions or suboptimal recommendations, industrial operators may make process changes that inadvertently increase rather than decrease energy use or emissions.Safeguard: The tool is designed to be used locally by industries on their own data, and model outputs are advisory — human engineers review recommendations before implementation. The semi-automatic data cleaning step also supports model quality.