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