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and Heating","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# NILMTK core\n\nNILMTK is the data and evaluation layer of the open-source ecosystem for\nnon-intrusive load monitoring (NILM). It converts and loads energy datasets,\nrepresents buildings and meters, prepares time windows, computes statistics and\nmetrics, and provides classical reference algorithms.\n\n**Use this repository when your work is about data, meters, preprocessing, or\nmetrics.** For maintained neural models or reproducible benchmark claims, use\nthe companion repositories below.\n\n## Ecosystem repositories\n\n| Research task | Repository |\n| --- | --- |\n| Dataset conversion, meter access, preprocessing, and metrics | **[NILMTK core](https://github.com/nilmtk/nilmtk)** — this repository |\n| Appliance taxonomy, synonyms, meter relationships, and dataset schema | [NILM Metadata](https://github.com/nilmtk/nilm_metadata) |\n| Disaggregation model implementation and testing | [nilmtk-contrib](https://github.com/nilmtk/nilmtk-contrib) |\n| Fixed T1/T2/T3 evaluation and published result bundles | [NILMbench](https://github.com/nilmtk/nilmbench) |\n\nThe [NILMTK ecosystem guide](https://nilmtk.github.io/) explains how these\nlayers fit together, which Docker route to use, and which papers to cite.\n\n## Supported installation\n\nNILMTK core supports Python 3.11 and newer. Use Python 3.11 for an end-to-end\nenvironment shared with nilmtk-contrib and NILMbench.\n\nInstall [uv](https://docs.astral.sh/uv/getting-started/installation/), then create\nan isolated environment:\n\n```bash\nuv venv --python 3.11\nsource .venv/bin/activate\nuv pip install \"nilmtk @ git+https://github.com/nilmtk/nilmtk.git\"\npython -c \"import nilmtk; print(nilmtk.__version__)\"\n```\n\nOn Windows PowerShell, activate the environment with\n`.venv\\Scripts\\Activate.ps1`.\n\nTo use the DEDDIAG converter, install the optional extra:\n\n```bash\nuv pip install \"nilmtk[deddiag] @ git+https://github.com/nilmtk/nilmtk.git\"\n```\n\nDo not combine these instructions with old Python 3.6, Anaconda-channel, or\n`setup.py develop` tutorials. Those routes describe earlier releases and are\nnot the supported installation for the current repository.\n\n### Verify the command-line converter\n\n```bash\nnilmtk-convert --help\nnilmtk-convert list\n# Example after downloading REDD:\nnilmtk-convert redd /path/to/low_freq /path/to/redd.h5\n```\n\nDataset-specific converter arguments and source links live under\n[`nilmtk/dataset_converters`](nilmtk/dataset_converters).\n\n## Docker ownership\n\nNILMTK core is a Python library and does not publish a separate official core\nimage. This is intentional:\n\n- use the single [nilmtk-contrib Dockerfile](https://github.com/nilmtk/nilmtk-contrib)\n  for a general environment containing core, metadata, and model code;\n- use [NILMbench](https://github.com/nilmtk/nilmbench) for pinned CPU-smoke and\n  CUDA-benchmark runtimes that certify leaderboard results;\n- do not create an image for each algorithm.\n\nKeeping container ownership in those two places prevents four repositories from\nshipping drifting copies of the same environment.\n\n## Data\n\nNILMTK does not redistribute REDD, UK-DALE, REFIT, or other licensed datasets.\nDownload data from its official custodian, comply with its license, and convert\nit locally. A converted HDF5 dataset can then be opened with:\n\n```python\nfrom nilmtk import DataSet\n\ndataset = DataSet(\"redd.h5\")\nprint(dataset.metadata)\nprint(dataset.buildings)\n```\n\nNILM Metadata is installed with core and supplies the canonical appliance\ntaxonomy, synonyms, and meter relationships used while loading datasets.\n\n## What core provides\n\n- converters for public energy datasets;\n- lazy access to buildings, meters, appliances, and time frames;\n- resampling, alignment, preprocessing, and data-quality statistics;\n- standard NILM accuracy and energy metrics;\n- the rapid experimentation API used by nilmtk-contrib;\n- classical reference disaggregators and baseline utilities.\n\nDetailed API reference is published at\n[nilmtk.github.io/nilmtk/master](https://nilmtk.github.io/nilmtk/master/index.html).\nThe repository manual and notebooks live under [`docs/manual`](docs/manual).\n\n## Development\n\n```bash\ngit clone https://github.com/nilmtk/nilmtk.git\ncd nilmtk\nuv sync --extra dev\nuv run pytest tests\n```\n\nBefore opening a pull request, run the narrow test for your change, the current\npackage gate, and the documentation contract:\n\n```bash\nuv run pytest tests\nuv run python scripts/check_docs.py\nuv build\n```\n\nThe historical core regression tests live under `nilmtk/tests` and\n`nilmtk/stats/tests`. Run the affected files explicitly when changing those\nmodules; work to bring those fixtures into the default gate is tracked\nseparately.\n\nChanges to dataset semantics belong in NILM Metadata. New model architectures\nbelong in nilmtk-contrib. Benchmark task definitions and published result\nbundles belong in NILMbench.\n\n## Citation\n\nIf you use core dataset conversion, meter abstractions, preprocessing, or\nmetrics, cite the NILMTK paper:\n\n```bibtex\n@inproceedings{batra2014nilmtk,\n  title     = {NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring},\n  author    = {Batra, Nipun and Kelly, Jack and Parson, Oliver and Dutta, Haimonti\n               and Knottenbelt, William and Rogers, Alex and Singh, Amarjeet\n               and Srivastava, Mani},\n  booktitle = {Proceedings of the 5th ACM International Conference on Future\n               Energy Systems},\n  year      = {2014},\n  pages     = {265--276},\n  doi       = {10.1145/2602044.2602051}\n}\n```\n\nAlso cite the [NILM Metadata paper](https://doi.org/10.1109/COMPSACW.2014.97)\nwhen relying on its schema or taxonomy, the\n[nilmtk-contrib paper](https://doi.org/10.1145/3360322.3360844) when using its\nmodel suite, and the [NILMBench2026 paper](https://doi.org/10.1145/3744256.3812587)\nwhen using its protocols, runner, or leaderboard results. Always cite the\noriginal model and dataset papers as well.\n\n## Help and license\n\nSearch [existing issues](https://github.com/nilmtk/nilmtk/issues) before opening\na report. 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