---
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 ACM e-Energy 2026 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.