CANOE-CEF

Author: Ian David Elder
Project: CANOE Model

This tool takes annual energy demand projections from the Canada's Energy Future (CEF) model and converts them into Temoa-compatible annual demands for the CANOE model.

For documentation: Visit here

Features

• Data Ingestion: Reads CEF demand data from CSV files.
• Data Transformation: Maps CEF regions, sectors, and commodities to CANOE model definitions.
• Database Output: Populates a Temoa-compatible SQLite database with the processed demand data.
• Electricity Distributions: Option to apply Demand Specific Distributions (DSD) for electricity.

Prerequisites

• Python 3.x
• pandas
• PyYAML

Installation

1. Clone the repository:

   git clone <repository-url>
   cd canoe-cef

2. Install the required Python packages:

   pip install -r requirements.txt

Configuration

Configuration is managed via files in the input_files/ directory, primarily params.yaml.

• params.yaml: Main configuration settings (scenario selection, database paths, etc.).
• regions.csv: Mapping of CEF regions to model regions.
• commodities.csv: Mapping of CEF variables/fuels to model commodities.
• sectors.csv: Mapping of CEF sectors to model sectors.
• end-use-demand-2023.csv: The source data from Canada's Energy Future.

Usage

To run the conversion process and populate the database:

python .

Or run the module directly:

python __main__.py

This will:

1. Initialize the SQLite database (or clear it if configured to do so).
2. Read the CEF input data.
3. Filter and aggregate data based on the configuration.
4. Write the Technology, Commodity, Demand, and Efficiency tables to the SQLite database.

Data Sources

• Canada's Energy Future (CEF): https://www.cer-rec.gc.ca/en/data-analysis/canada-energy-future/

Annual Updates

When updating for a new year:

1. Download the new end-use demand data from the CER website.
2. Replace the end-use-demand-XXXX.csv in input_files/.
3. Update params.yaml and mapping CSVs if scenario names or dimensions have changed.