refit-loader 1.2.2

A simple, fast and handy data loader for REFIT dataset to explore the data at convenience, provided with basic transformations like resampling and extract activities by thresholding.

REFIT Loader

This project uses Dask Dataframes to ease and fasten the process of loading all the data of REFIT and provides functionalities to transform and manipulate the REFIT dataset for statistical analysis purpose.

About REFIT dataset

An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study. Sci Data 4, 160122 (2017).
Murray, D., Stankovic, L. & Stankovic, V.

For more detail information, visit the following links:
http://dx.doi.org/10.1038/sdata.2016.122
https://rdcu.be/cMD9F

Dependencies

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

or create a new environment using 'environment.yml'

conda create env --file=environment.yml
conda activate refit_loader_env

Getting Started

1. Install the refit-loader in your current target environment

pip install refit-loader

2. Download the refit dataset. Import the REFIT_Loader and pass the data path to the refit object.

from refit_loader.data_loader import REFIT_Loader
refit = REFIT_Loader(data_path='')

3. Fetch the list of available appliances for selected houses.

refit.get_appliance_names(house=2)

4. Load data for selected appliance (all houses)

kettle = refit.get_appliance_data(appliance='Kettle')

5. (OR) Load data for selected house (all appliances).

house2 = refit.get_house_data(house=2)

6. (OR) Load data for sselected appliance and elected houses.

kettle = refit.get_appliance_data(appliance="Kettle", houses=[1,2,3])

7. Take the reference from Refit_Analyzer to see how Refit_Loader can be accessed and how it's utilities can be used.

Reference Repository:
Refit Analyzer = REFIT analyzer is more like a user guide that uses REFIT Loader as a submodule and demonstrates how it can be used and how it can be useful with its utilities.

Repo Structure:

This repository follows the below structure format:

|
|── data_loader.py
|
├── metadata
|  └── REFIT_README.txt
|
├── modules
|  └── active_durations.py
|  └── parser.py
|  └── validations.py
|  └── normalisation.py
|
├── environment.yml
|
├── readme.md
|

Downloads

The REFIT Smart Home dataset is a publicly available dataset of Smart Home data.
Dataset - https://pureportal.strath.ac.uk/files/52873459/Processed_Data_CSV.7z
Main Page - https://pureportal.strath.ac.uk/en/datasets/refit-electrical-load-measurements-cleaned

Citation

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