{"id":31225230,"url":"https://github.com/machinelearningbcam/load-forecasting-ieee-tpwrs-2020","last_synced_at":"2025-09-22T00:42:19.403Z","repository":{"id":39303977,"uuid":"315273238","full_name":"MachineLearningBCAM/Load-forecasting-IEEE-TPWRS-2020","owner":"MachineLearningBCAM","description":"Probabilistic Load Forecasting Based on Adaptive Online Learning (APLF)","archived":false,"fork":false,"pushed_at":"2024-03-11T15:36:52.000Z","size":3207,"stargazers_count":50,"open_issues_count":0,"forks_count":14,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-03-11T17:04:05.311Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MachineLearningBCAM.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-11-23T10:05:49.000Z","updated_at":"2024-02-29T16:49:14.000Z","dependencies_parsed_at":"2023-01-29T02:45:14.396Z","dependency_job_id":null,"html_url":"https://github.com/MachineLearningBCAM/Load-forecasting-IEEE-TPWRS-2020","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/MachineLearningBCAM/Load-forecasting-IEEE-TPWRS-2020","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningBCAM%2FLoad-forecasting-IEEE-TPWRS-2020","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningBCAM%2FLoad-forecasting-IEEE-TPWRS-2020/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningBCAM%2FLoad-forecasting-IEEE-TPWRS-2020/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningBCAM%2FLoad-forecasting-IEEE-TPWRS-2020/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MachineLearningBCAM","download_url":"https://codeload.github.com/MachineLearningBCAM/Load-forecasting-IEEE-TPWRS-2020/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MachineLearningBCAM%2FLoad-forecasting-IEEE-TPWRS-2020/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276329460,"owners_count":25623326,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-21T02:00:07.055Z","response_time":72,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-09-22T00:42:18.542Z","updated_at":"2025-09-22T00:42:19.393Z","avatar_url":"https://github.com/MachineLearningBCAM.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Probabilistic Load Forecasting based on Adaptive Online Learning (APLF)\n\n[![GitHub license](https://img.shields.io/badge/License-MIT-blue)](https://github.com/VeronicaAlvarez/online-probabilistic-load-forecasting/blob/master/LICENSE) [![Made with!](https://img.shields.io/badge/Made%20with-MATLAB-red)](APLF/Matlab) [![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](APLF/APLF.py) [![Ask Me Anything !](https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg)](#support-and-author)\n\nThis 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.\n\n![grab-landing-page](docs/images/predictions.gif)\n\n## Implementation of the method\n\nAPLF 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:\n\n* APLF.m is the main file.\n* initialize.m function inizializes model parameters.\n* prediction.m function obtain load forecasts and probabilistic load forecasts in form of mean and standard deviation of a Gaussian density function.\n* test.m function quantifies the prediction errors RMSE and MAPE.\n* update_model.m function updates the model for each new training sample.\n* update_parameters.m function updates model parameters.\n\n## Data\n\nWe use 7 publicly available datasets corresponding with regions that have different sizes. The datasets are:\n\n\u003e [Load demand in Belgium from 2017-2019 made available by Elia group.](https://www.elia.be/en/grid-data/data-download-page)  \n[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)  \n[Global Energy forecasting Competition 2012 dataset from 2004-2007.](http://blog.drhongtao.com/2016/07/gefcom2012-load-forecasting-data.html)  \n[Global Energy Forecasting Competition 2014 dataset from 2005-2011.](http://blog.drhongtao.com/2017/03/gefcom2014-load-forecasting-data.html)  \n[Load demand in Dayton from 2004-2016 made available by PJM interconnection.](https://www.pjm.com/markets-and-operations/data-dictionary.aspx)  \n[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)  \n[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)\n\nWe save the data in .mat files that contain a struct with following fields:\n\n* Hourly load time series\n* Temperature time series\n* Date and hour or timestamp when the load is measure\n\n## Installation\n\n```console\ngit clone https://github.com/VeronicaAlvarez/online-probabilistic-load-forecasting\n```\n\nRunning python code:\n```console\ncd online-probabilistic-load-forecasting\\APLF\npython APLF.py\n```\n\n\n## Test case\n\nWe 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.\n\n## Support and Author\n\nVerónica Álvarez Castro\n\nvalvarez@bcamath.org\n\n[![ForTheBadge built-with-science](http://ForTheBadge.com/images/badges/built-with-science.svg)](https://github.com/VeronicaAlvarez)\n\n## License \n\nLoad-forecasting-IEEE-TPWRS-2020 carries a MIT license.\n\n## Citation\n\nIf 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:\n\n\u003ca id=\"1\"\u003e[1]\u003c/a\u003e \nV. Alvarez, S. Mazuelas, J.A. Lozano.\n\"Probabilistic Load Forecasting based on Adaptive Online Learning,\"\n*IEEE-Transactions on Power Systems.* 2021.\n\nThe corresponding BiBTeX citation is given below:\n\n```\n@article{AlvMazLoz:21,\n title={Probabilistic Load Forecasting based on Adaptive Online Learning},\n author={Ver\\'{o}nica Alvarez and Santiago Mazuelas and Jos\\'{e} A. Lozano},\n journal={IEEE Transactions on Power Systems},\n year={2021},\n volume={36},\n number={4},\n month= {Jul.},\n pages={3668 -- 3680}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachinelearningbcam%2Fload-forecasting-ieee-tpwrs-2020","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmachinelearningbcam%2Fload-forecasting-ieee-tpwrs-2020","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachinelearningbcam%2Fload-forecasting-ieee-tpwrs-2020/lists"}