Greenhouse Climate Control Weather Parameters

Outside weather parameters for a reinforcement learning greenhouse climate control study. The dataset is 5.5 KB in size and contains features used to predict internal climate, crop growth, and resource consumption. It was created by Iman Hindi for a 2026 paper benchmarking RL algorithms like TD3.

Use Cases

• Train an LSTM model to forecast internal greenhouse climate conditions using the provided outside weather parameters.
• Analyze the influence of selected weather and control features on predicted crop parameters like stem elongation and cumulative trusses.
• Simulate a virtual greenhouse environment where an RL agent interacts with weather data to regulate temperature, CO2, and irrigation levels.
• Evaluate resource consumption metrics such as heating and electricity costs under stochastic weather conditions from the data.

Strengths

• Data is explicitly linked to a published research framework integrating MLP and LSTM models for prediction.
• Features were selected via a feature-selection mechanism to reduce observation complexity for RL training.
• Supports benchmarking of multiple RL algorithms (PPO, DDPG, SAC, TD3) as documented in the source paper.

Limitations

• Very small scale at 5.5 KB, indicating limited sample size or a minimal feature set.
• No row count, column names, or sample data are provided, hindering immediate assessment of structure and volume.
• Data is synthetic or simulated for a specific RL study, not real-world observational measurements.

Provenance

Source

figshare, author Iman Hindi.

Collection Method

Generated for a simulation study on RL-based greenhouse climate control.

Time Range

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Freshness

Last updated in March 2026.

Geography

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