Solar Irradiance Prediction with Meteorological Observations

SI Prediction Dataset using Ground ML Data for ML contains meteorological observations for predicting solar irradiance. The dataset is hosted on Kaggle, but specific details on its size, creator, and update frequency are not provided.

Use Cases

• Predict solar irradiance values using features like temperature, humidity, and wind speed.
• Train time-series models to forecast energy generation based on historical meteorological observations.
• Analyze the correlation between specific weather variables and ground-level solar irradiance measurements.

Strengths

• Data is specifically designed for machine learning tasks in solar energy prediction.

Limitations

• The dataset's size, number of rows, and column details are unknown, limiting assessment of its scope. The absence of sample data prevents verification of data structure and quality.

Provenance

Source

Kaggle

Collection Method

Assembled from ground-based meteorological observations, specific instruments or stations are unknown.