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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

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

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

CC0

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