https://github.com/machinelearningbcam/load-forecasting-ieee-tpwrs-2020
Probabilistic Load Forecasting Based on Adaptive Online Learning (APLF)
https://github.com/machinelearningbcam/load-forecasting-ieee-tpwrs-2020
Last synced: 10 months ago
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Probabilistic Load Forecasting Based on Adaptive Online Learning (APLF)
- Host: GitHub
- URL: https://github.com/machinelearningbcam/load-forecasting-ieee-tpwrs-2020
- Owner: MachineLearningBCAM
- License: other
- Created: 2020-11-23T10:05:49.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-03-11T15:36:52.000Z (over 2 years ago)
- Last Synced: 2024-03-11T17:04:05.311Z (over 2 years ago)
- Language: MATLAB
- Homepage:
- Size: 3.06 MB
- Stars: 50
- Watchers: 2
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Probabilistic Load Forecasting based on Adaptive Online Learning (APLF)
[](https://github.com/VeronicaAlvarez/online-probabilistic-load-forecasting/blob/master/LICENSE) [](APLF/Matlab) [](APLF/APLF.py) [](#support-and-author)
This repository contains code for the paper Probabilistic Load Forecasting based on Adaptive Online Learning [[1]](#1). We use the implementation details described in the paper.
## Implementation of the method
APLF folder contains the [Python file](APLF/APLF.py), a [Jupyter notebook](APLF/APLF.ipynb) and a [Matlab folder](APLF/Matlab) that contains all the Matlab scripts required to execute the method:
* APLF.m is the main file.
* initialize.m function inizializes model parameters.
* prediction.m function obtain load forecasts and probabilistic load forecasts in form of mean and standard deviation of a Gaussian density function.
* test.m function quantifies the prediction errors RMSE and MAPE.
* update_model.m function updates the model for each new training sample.
* update_parameters.m function updates model parameters.
## Data
We use 7 publicly available datasets corresponding with regions that have different sizes. The datasets are:
> [Load demand in Belgium from 2017-2019 made available by Elia group.](https://www.elia.be/en/grid-data/data-download-page)
[Load demand in New England from 2003-2014 made available by ISO-NE organization.](https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/-/tree/historical-hourly-flows-and-limits)
[Global Energy forecasting Competition 2012 dataset from 2004-2007.](http://blog.drhongtao.com/2016/07/gefcom2012-load-forecasting-data.html)
[Global Energy Forecasting Competition 2014 dataset from 2005-2011.](http://blog.drhongtao.com/2017/03/gefcom2014-load-forecasting-data.html)
[Load demand in Dayton from 2004-2016 made available by PJM interconnection.](https://www.pjm.com/markets-and-operations/data-dictionary.aspx)
[Load demand for 400 buildings in New South Wales from 2013 made available by the Australian Government.](http://dx.doi.org/10.17632/zm4f727vvr.1#file-a01cdaa0-340d-4ebf-8fe5-c59a53d8f6b0)
[Load demand for 100 buildings in New South Wales from 2013 made available by the Australian Government.](http://dx.doi.org/10.17632/zm4f727vvr.1#file-a01cdaa0-340d-4ebf-8fe5-c59a53d8f6b0)
We save the data in .mat files that contain a struct with following fields:
* Hourly load time series
* Temperature time series
* Date and hour or timestamp when the load is measure
## Installation
```console
git clone https://github.com/VeronicaAlvarez/online-probabilistic-load-forecasting
```
Running python code:
```console
cd online-probabilistic-load-forecasting\APLF
python APLF.py
```
## Test case
We display in this reposity an example for a dataset that contains load data of [400 buildings](https://data.mendeley.com/datasets/zm4f727vvr/1#file-a01cdaa0-340d-4ebf-8fe5-c59a53d8f6b0). [Example folder](/Example) includes more details of the dataset, commands to execute the code, and results.
## Support and Author
Verónica Álvarez Castro
valvarez@bcamath.org
[](https://github.com/VeronicaAlvarez)
## License
Load-forecasting-IEEE-TPWRS-2020 carries a MIT license.
## Citation
If you find useful the code in your research, please include explicit mention of our work in your publication with the following corresponding entry in your bibliography:
[1]
V. Alvarez, S. Mazuelas, J.A. Lozano.
"Probabilistic Load Forecasting based on Adaptive Online Learning,"
*IEEE-Transactions on Power Systems.* 2021.
The corresponding BiBTeX citation is given below:
```
@article{AlvMazLoz:21,
title={Probabilistic Load Forecasting based on Adaptive Online Learning},
author={Ver\'{o}nica Alvarez and Santiago Mazuelas and Jos\'{e} A. Lozano},
journal={IEEE Transactions on Power Systems},
year={2021},
volume={36},
number={4},
month= {Jul.},
pages={3668 -- 3680}
}
```