Hessen525 / nilm-papers-with-code

An archive for NILM papers with source code and other supplemental material

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?

Algorithms

HMM

• Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring (NILM). (2015). [pdf] [code]
  • S. Makonin et al. IEEE TSG.

Neural Nets

• Transfer Learning for Non-Intrusive Load Monitoring. (2019). [pdf] [code]

  • D. Michele et al. IEEE TSG.

• Neural NILM: Deep neural networks applied to energy disaggregation (2015) [pdf] [code]

  • J. Kelly et al. BuildSys'15

• Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks. (2018). [pdf] [code]

  • O. Krystalakos et al. Venue.

• Sequence-to-point learning with neural networks for non-intrusive load monitoring (2018) [pdf] [code]

  • C. Zhang et al. AAAI'18

• WaveNILM: A causal neural network for power disaggregation from the complex power signal (2019) [pdf] [code]

  • Alon Harell et al. ICASSP'19

Toolkits

• Towards reproducible state-of-the-art energy disaggregation. (2019) [pdf] [code]

  • N. Batra et al. BuildSys'19.

• NILM-Eval [pdf] [code]

• NILMTK [pdf] [code]

Metrics & Performance Evaluation

• Nonintrusive load monitoring (NILM) performance evaluation. (2015). [pdf] [code]

  • S. Makonin et al. Springer Energy Efficiency.

• Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation [pdf] [code]

  • C. Klemenjak et al. 2020 IEEE ISGT.

Misc

• 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.

• Metadata for Energy Disaggregation. (2014) [pdf] [code]

  • J. Kelly et al. CDS'14.

• SmartSim: A Device-Accurate Smart Home Simulator for Energy Analytics. (2016). [pdf] [code]

  • D. Chen et al. SmartGridComm'16.

• SynD: [link]

Datasets

• REDD [link]
• UK-DALE [link]
• BLUED [link]
• GREEND [link]
• AMPds [link]
• ECO [link]
• HES [link]
• Tracebase [link]
• PLAID [link]
• ENERTALK [link]

Licence

To the extent possible under law, Christoph Klemenjak has waived all copyright and related or neighbouring rights to this work.