marmossburg/solar-linear-regression

Solar Linear Regression

Model trained by _june-17/solar_linear_regression.py. PyTorch linear regression predicting daily solar energy output (kWh).

Dataset

Synthetic Colorado solar dataset generated by _june-17/generate_solar_dataset.py.

• Size: 500 rows
• Target: daily_energy_kwh (20.44 – 61.03 kWh)

Feature	Min	Max
day_of_year	1.00	365.00
sun_hours	4.02	8.50
cloud_cover_pct	0.40	59.90
panel_temp_c	5.30	70.00
system_size_kw	3.01	11.99

Performance

• Val R²: ~0.997
• Val RMSE: ~0.48 kWh

Files

• solar_best.pt — model weights, normalization stats, feature names
• solar_linear_regression.py — training script

Usage

import torch
from solar_linear_regression import LinearRegressionModel

ckpt = torch.load("solar_best.pt", map_location="cpu")
model = LinearRegressionModel(len(ckpt["feature_cols"]))
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

Normalize input with ckpt["X_mean"] / ckpt["X_std"], then denormalize output with ckpt["y_mean"] / ckpt["y_std"].

Limitations

Synthetic dataset; not based on real measured solar production.