| --- |
| 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. |
|
|
