README.md

metadata

license: cc-by-4.0
task_categories:
  - reinforcement-learning
  - tabular-classification
  - time-series-forecasting
language:
  - en
pretty_name: SolarChain-Eval
tags:
  - agentic-ai
  - cyber-physical-systems
  - decentralized-energy
  - reinforcement-learning
  - trustworthiness
  - false-data-injection
  - solar-energy
size_categories:
  - 1K<n<100K

SolarChain-Eval Dataset

SolarChain-Eval is a physics-constrained benchmark dataset for evaluating trustworthy economic agents in decentralized peer-to-peer solar energy markets.

The dataset supports the benchmark repository:

https://github.com/GreenComp-ERC/SolarChain-Eval

Intended Use

The dataset is intended for:

• evaluating autonomous economic governors in decentralized energy markets,
• studying trustworthiness metrics for agentic AI in cyber-physical systems,
• reproducing SolarChain-Eval experiments,
• testing reward-misspecification behavior under physics-informed constraints.

The dataset should not be used for real grid dispatch, financial trading, or operational market decisions.

Dataset Contents

data/
  datasets_2026_04_month/
    urban_energy_nodes.csv
    spatiotemporal_generation.csv
    market_liquidity.csv
    p2p_trades.csv
  datasets/
    urban_energy_nodes.csv
    spatiotemporal_generation.csv
    market_liquidity.csv
    p2p_trades.csv
  cache/
    open_meteo_weather_2026-04-01_2026-05-01.json
dataset_summary.json
checksums.sha256
DATA_LICENSE.md

data/datasets_2026_04_month/ is the main paper dataset. data/datasets/ is a small smoke-test dataset.

Main Dataset

• Time window: [2026-04-01, 2026-05-01)
• Cities: Beijing, Shanghai, Chengdu, Shenzhen, Hangzhou
• PV nodes: 50
• Hourly timestamps: 720
• Generation rows: 36,000
• Market rows: 720
• FDIA rows: 1,800
• Generation seed: 20260511

File Descriptions

urban_energy_nodes.csv

Synthetic urban PV node metadata:

• node_id
• city
• latitude
• longitude
• panel_area_m2
• efficiency
• temp_coefficient
• install_date

spatiotemporal_generation.csv

City-node-hour solar generation records:

• timestamp
• hour
• node_id
• city
• latitude
• longitude
• irradiance_Wm2
• air_temp_C
• P_max_W
• P_reported_W
• fdia_detected
• verification_status

market_liquidity.csv

Hourly aggregate market liquidity and slippage signals:

• timestamp
• hour
• total_verified_MW
• solarchain_liquidity_MW
• baseline_liquidity_MW
• slippage_solarchain_pct
• slippage_baseline_pct

p2p_trades.csv

Synthetic peer-to-peer trade demand records:

• trade_id
• timestamp
• hour
• factory_id
• city
• energy_purchased_MW
• tokens_burned
• exergy_dissipated_MJ

Open-Meteo Cache

data/cache/open_meteo_weather_2026-04-01_2026-05-01.json stores weather API responses used to generate the main monthly dataset. It is included to improve reproducibility and reduce dependence on live API availability.

Provenance

The dataset was generated by the SolarChain-Eval repository script:

scripts/generate_monthly_datasets.py

Weather inputs are derived from the Open-Meteo Historical Weather API. PV nodes, false-data injection labels, reported generation, market liquidity, and P2P trades are synthetic benchmark records generated deterministically from the stated seed.

Responsible AI Metadata

• Synthetic data: Yes.
• Personal or sensitive information: None. The dataset does not contain personal user records or real private transactions.
• Limitations: The benchmark covers five Chinese cities and one month. It is intended for controlled evaluation, not deployment.
• Biases: Results depend on city selection, the April 2026 weather window, synthetic PV node placement, synthetic trade demand, and the FDIA injection procedure.
• Use cases: Trustworthy agent evaluation, safety benchmarking, fairness analysis, reproducible RL experiments.
• Non-use cases: Real energy trading, grid dispatch, financial decisions, or operational market control.
• Social impact: The dataset is designed to support safer evaluation of agentic AI in cyber-physical markets. Misuse could occur if simulated outputs are mistaken for real operational data.

Checksums

Use checksums.sha256 to verify that downloaded files match the release package.

Example:

sha256sum -c checksums.sha256

On Windows PowerShell, compare against:

Get-FileHash -Algorithm SHA256 <path>

License

Dataset files are released under CC-BY-4.0. Benchmark code in the GitHub repository is released under MIT.