This project uses python package nilm-analyzer which has taken the advantage of Dask Dataframes to ease and fasten the process of loading all the data of any publicly available NILM dataset and also provides some basic transformation functionalities. This project demonstrates how nilm-analyzer can be used to do different transformations (e.g, resampling) and manipulate the datasets for statistical analysis purpose.
Link to the main repository: https://github.com/mahnoor-shahid/nilm_analyzer
Ensure that the following dependencies are satisfied either in your current environment
- python>=3.9.2
- numpy>=1.20.3
- pandas>=1.2.4
- dask>=2021.06.2
- scikit-learn>=1.1.2
- Install the nilm_analyzer in your current environment.
pip install nilm-analyzer
- Download any NILM dataset(s) and import the corresponding loader. Then, pass the data path of the data directory where the dataset is located. For instance,
from nilm_datasets.loaders import REFIT_Loader
refit = REFIT_Loader(data_path='data/refit/')
- Fetch the list of available appliances for selected houses.
refit.get_appliance_names(house=2)
- Load data for selected appliance (all houses)
kettle = refit.get_appliance_data(appliance='Kettle')
- (OR) Load data for selected house (all appliances).
house2 = refit.get_house_data(house=2)
- (OR) Load data for sselected appliance and elected houses.
kettle = refit.get_appliance_data(appliance="Kettle", houses=[1,2,3])
- To access the data, use the below command.
kettle.data
This repository follows the below structure format:
|
├── refit
| └── kettle
| | └── basic.ipynb
| | └── splits_for_training.ipynb
| | └── fetch_activations.ipynb
| └── microwave
|
├── 01_getting_started.ipynb
|
├── 02_resampling.ipynb
|
├── 03_extract_durations.ipynb
|
├── 04_normalization.ipynb
|
|
├── environment.yml
|
├── readme.md
|
REFIT [United Kingdom]
Murray, D., Stankovic, L. & Stankovic, V. An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study. Sci Data 4, 160122 (2017). https://doi.org/10.1038/sdata.2016.122
UK-DALE [United Kingdom]
Kelly, J., Knottenbelt, W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data 2, 150007 (2015). https://doi.org/10.1038/sdata.2015.7
GeLaP [Germany]
Wilhelm, S., Jakob, D., Kasbauer, J., Ahrens, D. (2022). GeLaP: German Labeled Dataset for Power Consumption. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_5
DEDDIAG [Germany]
Wenninger, M., Maier, A. & Schmidt, J. DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. Sci Data 8, 176 (2021). https://doi.org/10.1038/s41597-021-00963-2
AMPds [Canada]
S. Makonin, F. Popowich, L. Bartram, B. Gill and I. V. Bajić, "AMPds: A public dataset for load disaggregation and eco-feedback research," 2013 IEEE Electrical Power & Energy Conference, Halifax, NS, Canada, 2013, pp. 1-6, doi: 10.1109/EPEC.2013.6802949.
iAWE [India]
N. Batra, A. Singh, P. Singh, H. Dutta, V. Sarangan, M. Srivastava "Data Driven Energy Efficiency in Buildings"
REFIT [United Kingdom] https://pureportal.strath.ac.uk/files/52873459/Processed_Data_CSV.7z
UK-DALE [United Kingdom] http://data.ukedc.rl.ac.uk/simplebrowse/edc/efficiency/residential/EnergyConsumption/Domestic/UK-DALE-2017/UK-DALE-FULL-disaggregated/ukdale.zip
AMPds [Canada] https://dataverse.harvard.edu/api/access/datafile/2741425?format=original
GeLaP [Germany] https://mygit.th-deg.de/tcg/gelap/-/tree/master
DEDDIAG [Germany] https://figshare.com/articles/dataset/DEDDIAG_a_domestic_electricity_demand_dataset_of_individual_appliances_in_Germany/13615073
iAWE [India] https://drive.google.com/open?id=1c4Q9iusYbwXkCppXTsak5oZZYHfXPmnp