---
license: apache-2.0
tags:
- time-series-forecasting
- solar-energy
- chronos
- autogluon
- lora
- australia
library_name: autogluon
base_model: amazon/chronos-2
datasets:
- codenhenhe/volta-solar-daily-v1
---

# Chronos-2 Fine-tuned for Australian Solar Generation Forecasting

This repository contains a fine-tuned Chronos-2 model for daily solar generation forecasting in Australia. The model was fine-tuned using AutoGluon TimeSeriesPredictor and saved as an AutoGluon predictor directory.

## Model Details

- Base model: `amazon/chronos-2`
- Framework: AutoGluon TimeSeriesPredictor
- Fine-tuning method: LoRA
- Forecasting task: Daily solar generation forecasting
- Prediction length: 365 days
- Context length: 730 days
- Target variable: Daily solar generation
- Evaluation metric during training: MSE

## Input Features

The model uses historical solar generation together with temporal and weather-related covariates.

Known covariates include:

- `day_of_year`
- `month`
- `ALLSKY_SFC_SW_DWN`
- `T2M`
- `WS2M`
- `RH2M`

## Initial Metrics

The following metrics were obtained from the initial evaluation after fine-tuning. The model was evaluated on the 2024 and 2025 test periods.

### Point Forecast Accuracy

| Model | Period | MAE | MSE | RMSE | sMAPE (%) | R2 |
|---|---:|---:|---:|---:|---:|---:|
| Chronos Fine-Tuned | 2024 | 2.374061 | 11.042445 | 3.323017 | 10.768443 | 0.889873 |
| Chronos Fine-Tuned | 2025 | 2.468275 | 12.076611 | 3.475142 | 11.053124 | 0.883387 |
| Chronos Fine-Tuned | Overall | 2.421168 | 11.559528 | 3.399931 | 10.910783 | 0.886614 |

### Probabilistic Metrics

| Model | Period | Q_0.1 | Q_0.9 | Avg_Q_Loss |
|---|---:|---:|---:|---:|
| Chronos Fine-Tuned | 2024 | 0.601763 | 0.500016 | 0.550889 |
| Chronos Fine-Tuned | 2025 | 0.635956 | 0.506356 | 0.571156 |
| Chronos Fine-Tuned | Overall | 0.618859 | 0.503186 | 0.561023 |

## Baseline Comparison

The fine-tuned model was also compared against a seasonal naive baseline.

| Model | MAE | RMSE | MAPE (%) | R2 |
|---|---:|---:|---:|---:|
| Chronos2-Volta AI | 2.600748 | 3.640213 | 12.897249 | 0.870021 |
| Seasonal Naive Baseline | 6.080324 | 8.468857 | 29.693408 | 0.296490 |

## Loading the Model

This model is stored as an AutoGluon TimeSeriesPredictor directory. After downloading the repository, it can be loaded as follows:

```python
from autogluon.timeseries import TimeSeriesPredictor

predictor = TimeSeriesPredictor.load("path_to_downloaded_model")
```

## Intended Use

This model is intended for postcode-based daily solar generation forecasting in Australia. It can be used as part of a pipeline that maps a user postcode to a representative solar reference point, retrieves or simulates weather covariates, and forecasts future solar generation.

## Limitations

The model depends on the quality of postcode mapping, weather covariates, and historical solar generation data. Forecast accuracy may vary across regions, seasons, and unusual weather conditions.