Downloads · 30 days
0
solar-linear-regression
marmossburg/solar-linear-regression
solar-linear-regression is a machine learning model from marmossburg. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
Model trained by june-17/solarlinearregression.py. PyTorch linear regression predicting daily solar energy output (kWh).
Access
Public
Updated Jun 17, 2026
Repo size
238 KB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.png355 KB · 98%
At a glance
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jun 17, 2026
- Updated
- Jun 17, 2026
- SHA
- 5c1ebeea
Model card
From the Hugging Face model README
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 namessolar_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.
- Library
- pytorch
- License
- mit
- Languages
- en
- Created
- Jun 17, 2026
- Updated
- Jun 17, 2026