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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. |