klemenjak/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

Graph Signal Processing

• 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.

Mathematical Optimization

• Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring. (2022). [link] [code]
  • M. Balletti et al. IEEE TSG.*

Neural Nets

• Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network. (2021). [pdf] [code]

  • V. Piccialli et al. Energies

• Pruning Algorithms for Seq2Point Energy Disaggregation. (2020). [pdf] [code]

  • J. Barber et al. .

• 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

• Device-Free User Activity Detection using Non-Intrusive Load Monitoring: A Case Study. (2020). [pdf] [code]

  • A. Reinhardt et al. DFHS Workshop.

• 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.

• On time series representations for multi-label NILM. (2020) [pdf] [code]

  • C. Nalmpantis et al. Springer Neural Computing and Applications.

Datasets

Real-World Datasets

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

Synthetic Datasets and Generators

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

  • D. Chen et al. SmartGridComm'16.

• How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study. (2020). [pdf] [code]

  • A. Reinhardt et al. ACM e-energy.

• A synthetic energy dataset for non-intrusive load monitoring in households. (2020). [pdf] [code]

  • C. Klemenjak et al. Scientific Data.

Licence

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