Reproducibility of scientific contributions is an important aspect of scholarship that has received way to little attention! This repository aims to collect information on peer-reviewed NILM (alias energy disaggregation) papers that have been published with source code or extensive supplemental material. We group NILM papers based on a number of categories: algorithms, toolkits, datasets, and misc. Feel free to contribute to this repository! Please consider our "style guide":
- This is a title. (year). [pdf] [code]
- Main Author et al. Optional: Acronym of conference or journal i.e. Where was it published?
- On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing (2016). [pdf] [code]
- B. Zhao et al. IEEE Access.
Hidden Markov Models
- Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring (NILM). (2015). [pdf] [code]
- S. Makonin et al. IEEE TSG.
- Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring. (2022). [link] [code]
- M. Balletti et al. IEEE TSG.*
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Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network. (2021). [pdf] [code]
- V. Piccialli et al. Energies
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Pruning Algorithms for Seq2Point Energy Disaggregation. (2020). [pdf] [code]
- J. Barber et al. .
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Transfer Learning for Non-Intrusive Load Monitoring. (2019). [pdf] [code]
- D. Michele et al. IEEE TSG.
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Neural NILM: Deep neural networks applied to energy disaggregation (2015) [pdf] [code]
- J. Kelly et al. BuildSys'15
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Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks. (2018). [pdf] [code]
- O. Krystalakos et al. Venue.
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Sequence-to-point learning with neural networks for non-intrusive load monitoring (2018) [pdf] [code]
- C. Zhang et al. AAAI'18
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WaveNILM: A causal neural network for power disaggregation from the complex power signal (2019) [pdf] [code]
- Alon Harell et al. ICASSP'19
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Towards reproducible state-of-the-art energy disaggregation. (2019) [pdf] [code]
- N. Batra et al. BuildSys'19.
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Nonintrusive load monitoring (NILM) performance evaluation. (2015). [pdf] [code]
- S. Makonin et al. Springer Energy Efficiency.
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Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation [pdf] [code]
- C. Klemenjak et al. 2020 IEEE ISGT.
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Device-Free User Activity Detection using Non-Intrusive Load Monitoring: A Case Study. (2020). [pdf] [code]
- A. Reinhardt et al. DFHS Workshop.
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Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation, Artificial Intelligence Review (2018). [pdf] [code]
- C. Nalmpantis et al. Artificial Intelligence Review.
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Metadata for Energy Disaggregation. (2014) [pdf] [code]
- J. Kelly et al. CDS'14.
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On time series representations for multi-label NILM. (2020) [pdf] [code]
- C. Nalmpantis et al. Springer Neural Computing and Applications.
- REDD [link]
- UK-DALE [link]
- BLUED [link]
- GREEND [link]
- AMPds [link]
- ECO [link]
- HES [link]
- Tracebase [link]
- PLAID [link]
- ENERTALK [link]
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SmartSim: A Device-Accurate Smart Home Simulator for Energy Analytics. (2016). [pdf] [code]
- D. Chen et al. SmartGridComm'16.
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How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study. (2020). [pdf] [code]
- A. Reinhardt et al. ACM e-energy.
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A synthetic energy dataset for non-intrusive load monitoring in households. (2020). [pdf] [code]
- C. Klemenjak et al. Scientific Data.
To the extent possible under law, Christoph Klemenjak has waived all copyright and related or neighbouring rights to this work.