Improve dataset card: add paper link, GitHub repository, and sample usage

README.md

@@ -1,3 +1,81 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- other
+tags:
+- energy
+- NILM
+- solar-energy
+- smart-home
+---
+
+# PV-Augmented NILM Datasets
+
+[**Paper**](https://huggingface.co/papers/2508.14600) | [**GitHub**](https://github.com/MathAdventurer/PV-Augmented-NILM-Datasets)
+
+This repository contains synthesized photovoltaic (PV) augmented datasets for Non-Intrusive Load Monitoring (NILM). The toolkit enables researchers to create realistic scenarios of residential solar energy integration into public datasets (such as REDD and UK-DALE) to evaluate disaggregation algorithms under renewable energy conditions.
+
+This work was presented in the paper "[Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning](https://huggingface.co/papers/2508.14600)".
+
+## Overview
+
+The toolkit provides methods to:
+- Fetch real-world solar irradiance data from NREL's National Solar Radiation Database.
+- Simulate realistic PV system output with temperature effects.
+- Integrate PV injection with existing NILM datasets in NILMTK-compatible formats.
+
+## Sample Usage
+
+The following example shows how to process a NILM dataset with PV injection using the provided toolkit:
+
+```python
+from src.data_processor import process_dataset
+from src.weather_api import fetch_nrel_data
+from nilmtk import DataSet
+
+# Load weather data
+weather_data = fetch_nrel_data(
+    lat=42.3601,  # Boston, MA for REDD
+    lon=-71.0589,
+    year=2011
+)
+
+# Load NILM dataset
+redd = DataSet('path/to/redd.h5')
+
+# Process with PV injection
+data, train, test = process_dataset(
+    dataset_name='REDD',
+    dataset=redd,
+    building_number=1,
+    appliances=['microwave', 'fridge', 'dish washer', 'washing machine'],
+    train_start_str='2011-04-19',
+    train_end_str='2011-05-03',
+    test_start_str='2011-05-04',
+    test_end_str='2011-05-11',
+    weather_data=weather_data,
+    pv_capacity=2000  # 2kW system
+)
+```
+
+## Citation
+
+If you use this toolkit or dataset in your research, please cite:
+
+```bibtex
+@misc{wang2025energy,
+      title={Energy Injection Identification Enabled Disaggregation with Deep Multi-Task Learning}, 
+      author={Xudong Wang and Guoming Tang and Junyu Xue and Srinivasan Keshav and Tongxin Li and Chris Ding},
+      year={2025},
+      eprint={2508.14600},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2508.14600}, 
+}
+```
+
+## Acknowledgments
+
+- National Renewable Energy Laboratory (NREL) for providing the NSRDB API.
+- NILMTK team for the toolkit and dataset support.
+- REDD and UK-DALE dataset creators.