├── .github └── FUNDING.yml └── README.md /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] 4 | patreon: # Replace with a single Patreon username 5 | open_collective: # Replace with a single Open Collective username 6 | ko_fi: # Replace with a single Ko-fi username 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: # Replace with a single Liberapay username 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | custom: # 13 | 14 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Tip Me via PayPal](https://img.shields.io/badge/PayPal-tip%20me-green.svg?logo=paypal)](https://www.paypal.me/ChristophKlemenjak) 2 | 3 | ![](http://wwwu.aau.at/chklemen/Untitled-49.png) 4 | 5 | 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": 6 | 7 | - **This is a title.** (year). [[pdf]](link-to-pdf) [[code]](link-to-code) 8 | - Main Author et al. Optional: *Acronym of conference or journal* i.e. Where was it published? 9 | 10 | 14 | 15 | 16 | ## Algorithms 17 | 18 | ### Graph Signal Processing 19 | 20 | - **On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing** (2016). [[pdf]](https://ieeexplore.ieee.org/document/7457610) [[code]](https://github.com/loneharoon/GSP_energy_disaggregator) 21 | - B. Zhao et al. *IEEE Access.* 22 | 23 | ### Hidden Markov Models 24 | 25 | - **Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring (NILM).** (2015). [[pdf]](http://makonin.com/doc/TSG_2015.pdf) [[code]](https://github.com/smakonin/SparseNILM) 26 | - S. Makonin et al. *IEEE TSG.* 27 | 28 | ### Mathematical Optimization 29 | 30 | - **Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring.** (2022). [[link]](https://ieeexplore.ieee.org/document/9714495) [[code]](https://github.com/antoniosudoso/nilm-bqp) 31 | - M. Balletti et al. *IEEE TSG.** 32 | 33 | ### Neural Nets 34 | 35 | - **Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network.** (2021). [[pdf]](https://www.mdpi.com/1996-1073/14/4/847/pdf) [[code]](https://github.com/antoniosudoso/attention-nilm) 36 | - V. Piccialli et al. *Energies* 37 | 38 | - **Pruning Algorithms for Seq2Point Energy Disaggregation.** (2020). [[pdf]]() [[code]](https://github.com/JackBarber98/pruned-nilm) 39 | - J. Barber et al. *.* 40 | 41 | - **Transfer Learning for Non-Intrusive Load Monitoring.** (2019). [[pdf]]() [[code]](https://github.com/MingjunZhong/transferNILM) 42 | - D. Michele et al. *IEEE TSG.* 43 | 44 | - **Neural NILM: Deep neural networks applied to energy disaggregation** (2015) [[pdf]](http://jack-kelly.com/files/writing/neural_nilm.pdf) [[code]](https://github.com/JackKelly/neuralnilm) 45 | - J. Kelly et al. *BuildSys'15* 46 | 47 | - **Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks.** (2018). [[pdf]](https://dl.acm.org/citation.cfm?doid=3200947.3201011) [[code]](https://github.com/OdysseasKr/online-nilm) 48 | - O. Krystalakos et al. *Venue.* 49 | 50 | - **Sequence-to-point learning with neural networks for non-intrusive load monitoring** (2018) [[pdf]](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16623/15980) [[code]](https://github.com/MingjunZhong/NeuralNetNilm) 51 | - C. Zhang et al. *AAAI'18* 52 | 53 | - **WaveNILM: A causal neural network for power disaggregation from the complex power signal** (2019) [[pdf]](https://arxiv.org/pdf/1902.08736.pdf) [[code]](https://github.com/picagrad/WaveNILM) 54 | - Alon Harell et al. *ICASSP'19* 55 | 56 | 57 | ## Toolkits 58 | 59 | - **Towards reproducible state-of-the-art energy disaggregation.** (2019) [[pdf]](https://nipunbatra.github.io/papers/batra_buildsys_19.pdf) [[code]](https://github.com/nilmtk/nilmtk-contrib) 60 | - N. Batra et al. *BuildSys'19.* 61 | 62 | 63 | - NILM-Eval [[pdf]]() [[code]](https://github.com/beckel/nilm-eval) 64 | - NILMTK [[pdf]](https://arxiv.org/pdf/1404.3878v1.pdf) [[code]](https://github.com/nilmtk/nilmtk) 65 | 66 | ## Metrics & Performance Evaluation 67 | 68 | - **Nonintrusive load monitoring (NILM) performance evaluation.** (2015). [[pdf]](https://link.springer.com/article/10.1007%2Fs12053-014-9306-2) [[code]](https://github.com/smakonin/NILM_PerformanceEval) 69 | - S. Makonin et al. *Springer Energy Efficiency.* 70 | 71 | - **Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation** [[pdf]](http://makonin.com/doc/ISGT-NA_2020b.pdf) [[code]]() 72 | - C. Klemenjak et al. 2020 IEEE ISGT. 73 | 74 | ## Misc 75 | 76 | - **Device-Free User Activity Detection using Non-Intrusive Load Monitoring: A Case Study.** (2020). [[pdf]](https://www.areinhardt.de/publications/2020/Reinhardt_DFHS_2020.pdf) [[code]](https://github.com/klemenjak/antgen) 77 | - A. Reinhardt et al. *DFHS Workshop.* 78 | 79 | - **Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation, Artificial Intelligence Review** (2018). [[pdf]](https://intelligence.csd.auth.gr/publications/machine-learning-approaches-for-non-intrusive-load-monitoring-from-qualitative-to-quantitative-comparation/) [[code]](https://github.com/ChristoferNal/power-disaggregation-complexity) 80 | - C. Nalmpantis et al. *Artificial Intelligence Review.* 81 | 82 | - **Metadata for Energy Disaggregation.** (2014) [[pdf]](https://ieeexplore.ieee.org/document/6903193) [[code]](https://github.com/nilmtk/nilm_metadata) 83 | - J. Kelly et al. *CDS'14.* 84 | 85 | - **On time series representations for multi-label NILM.** (2020) [[pdf]](https://link.springer.com/epdf/10.1007/s00521-020-04916-5?sharing_token=bTZg6CBADDbWx7UVvztexPe4RwlQNchNByi7wbcMAY4YyOCPZ8jI-u3LyC4lDtEOZIQACACm_MVY_633J4jzg0CtjGEkhvPkzOs5Z-2UGgB1P_m1_4nDnPxtIplmNRaDx7TM52V6MVQYVJPSqJEKpxv1n3RqXoEm1ZpW5amjaaA%3D) [[code]](https://github.com/ChristoferNal/multi-nilm) 86 | - C. Nalmpantis et al. *Springer Neural Computing and Applications.* 87 | 88 | ## Datasets 89 | 90 | #### Real-World Datasets 91 | 92 | - REDD [[link]](https://web.archive.org/web/20220812015008/http://redd.csail.mit.edu/) 93 | - UK-DALE [[link]](https://www.nature.com/articles/sdata20157) 94 | - BLUED [[link]](http://portoalegre.andrew.cmu.edu:88/BLUED/) 95 | - GREEND [[link]](https://sourceforge.net/projects/greend/) 96 | - AMPds [[link]](http://ampds.org/) 97 | - ECO [[link]](http://www.vs.inf.ethz.ch/res/show.html?what=eco-data) 98 | - HES [[link]](http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&ProjectID=17359&FromSearch=Y&Publisher=1&SearchText=EV0702&SortString=ProjectCode&SortOrder=Asc&Paging=10#Description) 99 | - Tracebase [[link]](https://github.com/areinhardt/tracebase) 100 | - PLAID [[link]](http://www.plaidplug.com/) 101 | - ENERTALK [[link]](https://www.nature.com/articles/s41597-019-0212-5) 102 | 103 | 104 | #### Synthetic Datasets and Generators 105 | 106 | - **SmartSim: A Device-Accurate Smart Home Simulator for Energy Analytics.** (2016). [[pdf]](http://www.ecs.umass.edu/~irwin/smartsim.pdf) [[code]](https://github.com/sustainablecomputinglab/smartsim) 107 | - D. Chen et al. *SmartGridComm'16.* 108 | 109 | - **How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study.** (2020). [[pdf]](https://www.areinhardt.de/publications/2020/Reinhardt_eEnergy_2020.pdf) [[code]](https://github.com/klemenjak/antgen) 110 | - A. Reinhardt et al. *ACM e-energy.* 111 | 112 | - **A synthetic energy dataset for non-intrusive load monitoring in households.** (2020). [[pdf]](https://www.nature.com/articles/s41597-020-0434-6) [[code]](https://github.com/klemenjak/SynD) 113 | - C. Klemenjak et al. *Scientific Data.* 114 | 115 | 116 | ## Licence 117 | [![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/) 118 | 119 | To the extent possible under law, [Christoph Klemenjak](https://github.com/klemenjak) has waived all copyright and related or neighbouring rights to this work. 120 | --------------------------------------------------------------------------------