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My solution to solve the second IEEE-CIS technical challenge

Jupyter Notebook 99.07% Python 0.93%

cis-challenge-energy-prediction's Introduction

IEEE-CIS Second Technical Challenge Solution

- Energy prediction from smart meter data

Wenlong Wu, EECS, University of Missouri

Notebooks:

  • eda-figure.ipynb: exploratory data analysis and generated visualization figures;
  • tsne-visualization.ipynb: t-SNE dimension reduction and visualization;
  • constant_prediction.ipynb: generate constant prediction using Nov and Dec data;
  • preprocess-data-clean.ipynb: data preprocessing;
  • feature-engineering.ipynb: feature engineering;
  • modeling-lgb.ipynb: modeling using LightGBM model;
  • mini_lgb_model.ipynb: modeling using 12 LightGBM models for each cluster;
  • check_outliers.ipynb: detect suspicious meters;
  • post-processing.ipynb: post processing;
  • ensemble.ipynb: ensemble multiple submissions;
  • visual_submission.ipynb: generate visualization figures for visualization;

Competition page:

https://ieee-dataport.org/competitions/ieee-cis-technical-challenge-energy-prediction-smart-meter-data

I ranked first position on the final evaluation:

https://engineering.missouri.edu/2020/12/mizzou-engineering-student-takes-first-place-in-ieee-computational-challenge/

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