Energy Test Data

Preparation of test data for energy trading strategy demo.

Overview

This project generates and processes realistic test data for energy trading strategies, including:

• Electricity Prices: Day-ahead and real-time market prices for European regions (FR, BE, DE, NL, UK)
• Battery Capacity: Storage system states with charge/discharge cycles
• Renewable Generation: Solar, wind, and hydro generation with forecast errors
• Conventional Generation: Gas, coal, and nuclear plant outputs
• Load Profiles: Regional electricity demand with weather correlations
• Data Centers: Power demand profiles including Bitcoin mining client
• Mining: Hashrate, price, and profitability data (from mempool.space)

Project Structure

energy-test-data/
├── data/
│   ├── processed/              # Final Parquet files (<200MB total)
│   ├── raw/                    # Unprocessed source data
│   └── metadata/               # Data documentation and reports
├── scripts/
│   ├── 01_generate_synthetic.py    # Generate synthetic data
│   ├── 02_fetch_historical.py      # Fetch historical data
│   ├── 03_process_merge.py         # Process and compress
│   └── 04_validate.py              # Validate and report
├── config/
│   ├── data_config.yaml            # Configuration parameters
│   └── schema.yaml                 # Data schema definitions
├── requirements.txt
└── README.md

Installation

pip install -r requirements.txt

Usage

Generate all test data

Run scripts in sequence:

python scripts/01_generate_synthetic.py
python scripts/02_fetch_historical.py
python scripts/03_process_merge.py
python scripts/04_validate.py

Or run all at once:

python scripts/01_generate_synthetic.py && \
python scripts/02_fetch_historical.py && \
python scripts/03_process_merge.py && \
python scripts/04_validate.py

Individual scripts

01_generate_synthetic.py: Creates synthetic data for battery systems, renewable generation, conventional generation, and data centers.

02_fetch_historical.py: Fetches electricity prices, mining data, and load profiles from public APIs (or generates realistic synthetic data when APIs are unavailable).

03_process_merge.py: Merges datasets, optimizes memory usage, and saves to compressed Parquet format.

04_validate.py: Validates data quality, checks for missing values and outliers, and generates validation reports.

Configuration

Edit config/data_config.yaml to customize:

• Time range: Start/end dates and granularity
• Regions: Market regions to include
• Data sources: Synthetic vs historical for each dataset
• Generation parameters: Noise levels, outlier rates, missing value rates
• Battery parameters: Capacity ranges, efficiency, degradation
• Generation parameters: Plant capacities, marginal costs
• Mining parameters: Hashrate ranges, mining efficiency

Data Specifications

Dataset	Time Range	Rows (10d × 1min)	Est. Size
electricity_prices	10 days	72,000	~40MB
battery_capacity	10 days	144,000	~20MB
renewable_generation	10 days	216,000	~35MB
conventional_generation	10 days	144,000	~25MB
load_profiles	10 days	72,000	~30MB
data_centers	10 days	72,000	~15MB
mining	10 days	14,400	~20MB
Total			~185MB

Output Format

All processed datasets are saved as Parquet files with Snappy compression in data/processed/.

To read a dataset:

import pandas as pd

df = pd.read_parquet('data/processed/electricity_prices.parquet')
print(df.head())

Data Sources

• Electricity Prices: Hybrid (synthetic patterns based on EPEX Spot market characteristics)
• Mining: Hybrid (mempool.space API + synthetic patterns)
• Load Profiles: Hybrid (ENTSO-E transparency platform patterns + synthetic)

Validation Reports

After processing, validation reports are generated in data/metadata/:

• validation_report.json: Data quality checks, missing values, range violations
• final_metadata.json: Dataset sizes, row counts, processing details