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DistrNNEnergy.jl

This repository supports the paper Barunik, Jozef and Hanus, Luboš, Learning Probability Distributions of Day-Ahead Electricity Prices. Available at SSRN: link. We provide functions and full exercise code to obtain the results. The results might not be exactly the same numerically, since it depends on the random seed and initializations of neural network parameters.

The process how to activate and use this project code is below. The code is build in Julia v1.8.5.

How to work with the code in julia below, go ⬇.

Learning Distribution of Electricity Prices

Open a file you want to run and go step-by-step, or just run bash commands to execute whole code from terminal. Also, there is a notebook with minimum working example in /notebooks.

The main code, to replicate the study is scripts/run_complete_par.jl. It runs parallel CPU processes to perform hyper-optimization and forward rolling scheme over out-of-sample days, where it trains neural networks for each day.

Further, in scripts, we provide a code to prepare data frames X and Y we use in the learning, scripts/data_prepare.jl.

Simple estimation (one day and one hour)

We provide a working minimum example at notebooks/minimum_example/minimum_example.md and as notebook in /notebooks.

One can train the network for one day and one hour using run_simple.jl, which is a script containing the same code as the notebook. It disaggregates the code from complete run file, also provides simple plots and evaluation functions.

Full run

RUN in terminal/bash:

# _ full file to run, takes 24-36 hours on 60 cores:
julia scripts/run_complete_par.jl > data/temp_"`date +%FT%H%M`".txt

# _ One can use nohup to run the code in background (nohup and `&`):
nohup julia scripts/run_complete_par.jl > data/temp_"`date +%FT%H%M`".txt &

The complete script saves results into .csv and .bson files into folder data/exp_pro.

Notes

Please, be aware that during the execution ensembles estimation for individual hours, hence days in the loop, there might occur a memory overflow. Although, tt does not happen often, it may happen. If so, just start the loop over hours 1:24 at hour when it happened, like 14:24.

TODO

  • Polish code
  • Upload LEAR QRA in Julia, now we ask to use epftoolbox written in python.
    • Jesus Lago, Grzegorz Marcjasz, Bart De Schutter, Rafał Weron. “Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark”. Applied Energy 2021; 293:116983.

How to install and use this package

One approach to use the project

This is a simple approach how to instantiate the project and install its dependencies. This is also required before one opens the notebook example.

  1. Open a Julia console in the folder of this repository.
  2. Activate the project's environment
using Pkg
Pkg.activate(".")
  1. And instantiate the project, which will install all necessary packages and their correct versions.
Pkg.instantiate()
  1. Open the script scripts/run_simple_day_hour.jl in your favourite editor and copy+paste or execute the code in the Julia console

Jupyter Notebooks/Lab: Be aware that if you want to use julia in Jupyter Notebooks or Jupyter Lab, use first need to add IJulia package to your global julia environment. In a Julia console run this:

using Pkg
Pkg.add("IJulia")

This install the julia kernel and then you may start your Jupyter Notebook/Lab.

DrWatson manual (alternative approach to the repository)

This code base is using the Julia Language and DrWatson to make a reproducible scientific project.

To (locally) reproduce this project, do the following:

  1. Download this code base. Notice that raw data are typically not included in the git-history and may need to be downloaded independently. (We provide the data in data/exp_raw.)
  2. Open a Julia console and do: 
    julia> using Pkg
julia> Pkg.add("DrWatson") # install globally, for using `quickactivate`
julia> Pkg.activate("path/to/this/project")
julia> Pkg.instantiate()

This will install all necessary packages for you to be able to run the scripts and everything should work out of the box, including correctly finding local paths.

You may notice that most scripts start with the commands:

using DrWatson
@quickactivate "DistrNNEnergy"

which auto-activate the project and enable local path handling from DrWatson.

Other alternative

It is just to open this project in VSCode or Atom editors and it will locate your julia console at the folder once use execute/run a line of julia code from one of the scripts.