Package: ifeel
Interpretable Feature Extraction of Electricity Loads (IFEEL)A python package forInterpretable Feature Extraction of Electricity Loads(IFEEL).IFEEL has a similar pronunciation to the Eiffel Tower 🗼 so you will find two "Eiffel" electricity towers in the IFEEL logo.Description of IFEEL can be found onGitHub🔗(Recommended, no image loading issue) orPyPI🔗📌 Illustration:Note: If the picture fails to load, please clickhere.⚙️ Installation:You can usepipto easily install IFEEL with:pip install ifeelMore info aboutpipcan be foundhere.🤖 Developer info:Package title: Interpretable Feature Extraction of Electricity Load (IFEEL)Authors:Maomao Hu,Dongjiao Ge,David WallomOrganization:Oxford e-Research Center, Department of Engineering Science, University of OxfordContact info:maomao.hu@eng.ox.ac.ukDevelopment time: Oct 2020Acknowledgement: This work was financially supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant (EP/S030131/1) ofAMIDINE. We also would like to thank the help fromBruce Stephen,Jethro Browell,Rory Telford,Stuart Galloway, andCiaran Gilbertfrom the University of Strathclyde.💬 About IFEEL:(1) This Python package (i.e., IFEEL) aims to help energy data analysts to readily extract interpretable features of daily electricity profiles from a physical perspective. The extracted features can be applied for further feature-based machine learning purposes, including feature-based PCA, clustering, classification, and regression.(2) Two PY files (.py) are included in the IFEEL package, includingifeel_transformation.pyandifeel_extraction.py.(3) Two types of features, including 13 global features (GFs) and 8 peak-period features (PFs), can be extracted by using this package. Detailed description of all features can be found in Ref [1] or the Demo file in the installed IFEEL package.(4) GFs are extracted based on raw time-series data, while PFs are extracted based on symbolic representation of time series data. GFs and PFs can be obtained by usingIFEEL.ifeel_extraction.feature_globalandIFEEL.ifeel_extraction.feature_peak_period, respectively.(5) For fast peak-period feature extraction, Symbolic Aggregate approXimation (SAX) representation is first used to transform the time-series numerical patterns into alphabetical words. The feature transformation process is performed by callingIFEEL.ifeel_transformation.feature_transformation. More details about SAX approach can be found in Ref [2] and Ref [3].🔈 Notes:(1) To successfully run the IFEEL, the following Python data analysis libraries need to be installed in advance:Numpy,Scipy, andPandas.(2) ADemocan be found in the installed IFEEL package orhere. Three datasets at different time intervals can be downloadedhere, and tested in the Demo.(3) TheDemohas been tested on Python 3.7.7.📚 References[1] Hu M, Ge D, Telford R, Stephen B, Wallom D. Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering.Sustainable Cities and Society. 2021;75:103380.[2] Lin J, Keogh E, Wei L, Lonardi S. Experiencing SAX: a novel symbolic representation of time series.Data Mining and Knowledge Discovery. 2007;15:107-44.[3] Keogh E, Lin J, Fu A. HOT SAX: efficiently finding the most unusual time series subsequence.5TH IEEE International Conference on Data Mining (ICDM'05). 2005. p8.