demand-comp-cntry

A competition for forecasting electricity demand at the country-level using a standard backtesting framework

Introduction

This repo makes scripts available for downloading and compiling country-level data to be used in electricity demand forecasting at the country level. The goal of this repo is to encourage collaborative and competitive efforts towards the use of machine learning backtesting frameworks for forecasting electricity consumption and to use trained models to predict future consumption at the country-level. The results of such models can be used directly as input to the pypsa-africa repository and other similar modeling efforts.

Technical Background

Before getting started, it is recommended that users of and contributors to this repository have some background on backtesting, cross-validation, and probabilistic forecasting. Here are a few links to get started:

• backtesting: https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/
• cross-validation: https://arxiv.org/abs/1811.12808
• probabilistic forecasting for demand: Lee et al. working paper, https://www.energyeconomicgrowth.org/sites/default/files/2021-09/Lee_et_al-How_probabilistic_electricity_demand_forecasts.pdf

Setup

• Set up an appropriate .env file in the project root. This is listed in .gitignore, and as such, is ignored by Git. PROJECT_ROOT, PROJECT_CACHE, and PROJECT_OUT directories must be specified. For example,

  touch .env
  

  And, for example, populate this as follows:

  PROJECT_ROOT=
       
        
  PROJECT_CACHE=
        
         
  PROJECT_OUT=
         
  
         
        
       

• Make appropriate cache and out folders:

  cd 
       
        
  mkdir cache
  mkdir out  
  
       

• Download data

  python demand/data/download_data.py
  

• Ensure you have World energy statistics (Edition 2020) data, filename iea_wes_2020-68578195-en.zip, in directory /data/raw/iea_wes_2020-68578195-en.zip
• Ensure you have World energy balances (Edition 2020) data, filename iea_web_2020-cde01922-en.zip, in directory /data/raw/iea_web_2020-cde01922-en.zip
• Install conda environment:

  conda env create -f environment.yml
  

• Run test script using ARIMA models:

  cd 
       
        
  python demand/models/run_arima.py