## Project description

ProLoaF is a probabilistic load forecasting project.

## Description

ProLoaF makes use of the big data paradigm that allows machine learning algorithms, trained with data from the power system field. The core comprises a recurrent neural network (encoder-decoder architecture) to predict the target variable.

The targets can vary from timerseries of PV, Wind or other generators to most commonly the total energy consumption. Both, the relevant input data history and prediction horizon are arbitrarily long and customizable for any specific need.

## Installation

First, clone this Repository and initialize the submodules:

git clone --recurse-submodule https://git.rwth-aachen.de/acs/public/automation/plf/proloaf.git


or if you have already cloned the project and you are missing e.g. the open data directory for execution run:

git submodule update --init --recursive


To install all required packages first run:

pip install -r requirements.txt


On low RAM machines the option

pip install -r requirements.txt --no-cache-dir


might be necessary.

ProLoaF supports Python 3.8 and higher. For further information see Getting Started

## Gettings started, Key Capabilities, Example Workflows & References

To keep all infos on what you can do with ProLoaF and how to get there, our user documentation, is the place where you'll find all answers.

## Related Publications

We kindly ask all academic publications employing components of ProLoaF to cite one of the following papers:

• G. GÃ¼rses-Tran, H. Flamme, and A. Monti, "Probabilistic Load Forecasting for Day-Ahead Congestion Mitigation," The 16th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020, 2020-08-18 - 2020-08-21, LiÃ¨ge, Belgium, ISBN 978-1-7281-2822-1, [Access Online](Probabilistic Load Forecasting for Day-Ahead Congestion Mitigation)

2019-2021, Institute for Automation of Complex Power Systems, EONERC

This project is licensed to the Apache Software Foundation (ASF).

This program is free software: you can redistribute it and/or modify
it under the terms of the Apache License version 2 of the License, or
any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the



For other licensing options please consult Prof. Antonello Monti.

## Project details

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