Datasetpublic

codechimanshu/smartgrid-openenv-dataset

Smart Grid Energy Trader — Dataset Complete training and evaluation dataset for the Smart Grid OpenEnv environment. Files File Rows Description states.csv 216 All environment states across all tasks episodes.csv 3,860 Full episode rollouts with agent decisions price_patterns.csv 96 24-hour price curves for 4 market scenarios llm_prompts.jsonl 500 LLM training examples in OpenAI chat format states.csv columns Column Type… See the full description on the dataset page: https://huggingface.co/datasets/codechimanshu/smartgrid-openenv-dataset.

sourceHugging Faceupdated 6mo agoView on Hugging Face

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Smart Grid Energy Trader — Dataset

Complete training and evaluation dataset for the Smart Grid OpenEnv environment.

Files

File	Rows	Description
`states.csv`	216	All environment states across all tasks
`episodes.csv`	3,860	Full episode rollouts with agent decisions
`price_patterns.csv`	96	24-hour price curves for 4 market scenarios
`llm_prompts.jsonl`	500	LLM training examples in OpenAI chat format

states.csv columns

Column	Type	Description
task_id	int	1=easy, 2=medium, 3=hard
day	int	Day number (1–7)
hour	int	Hour (0–23)
weather	str	sunny / cloudy / stormy
solar_output	float	kWh from solar this hour
wind_output	float	kWh from wind this hour
generation	float	Total kWh generated
grid_price	float	Rs per kWh
home_demand	float	kWh homes need
price_zone	str	peak / mid / off_peak
isstormday	int	1 if stormy (Task 3 Days 4–5)
isspikeday	int	1 if price spike (Task 3 Day 6)

episodes.csv columns

All state columns above, plus:

Column	Type	Description
episode_id	int	Unique episode identifier
seed	int	Random seed used
battery_level	float	Battery % at this step
rule_action	int	Rule-based agent decision
ruleactionname	str	STORE / DISTRIBUTE / SELL
rule_reward	float	Reward from rule-based action
optimal_action	int	Optimal agent decision
optimalactionname	str	STORE / DISTRIBUTE / SELL
optimal_reward	float	Reward from optimal action
reward_gap	float	How much better optimal was

llm_prompts.jsonl format

json

{
  "messages": [
    {"role": "system",    "content": "You manage a microgrid..."},
    {"role": "user",      "content": "Current state:\n{...}"},
    {"role": "assistant", "content": "{\"action\": 2}"}
  ],
  "optimal_action": 2,
  "optimal_action_name": "SELL",
  "reason": "Grid price is high (11.5 Rs/kWh). Sell to maximize revenue.",
  "task_id": 2
}

Regenerate

bash

python generate_dataset.py