{"id":13538382,"url":"https://github.com/pyaf/load_forecasting","last_synced_at":"2025-04-02T05:31:12.698Z","repository":{"id":28322572,"uuid":"118086285","full_name":"pyaf/load_forecasting","owner":"pyaf","description":"Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models","archived":false,"fork":false,"pushed_at":"2022-12-08T01:29:06.000Z","size":21285,"stargazers_count":475,"open_issues_count":16,"forks_count":156,"subscribers_count":13,"default_branch":"master","last_synced_at":"2024-06-11T17:43:26.969Z","etag":null,"topics":["arima","electric-load-forecasting","gru","lstm","machine-learning","rnn","ses","sma","time-series-forecasting","wma"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pyaf.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":"2018-01-19T06:26:42.000Z","updated_at":"2024-06-11T14:57:49.000Z","dependencies_parsed_at":"2022-07-31T23:28:00.037Z","dependency_job_id":null,"html_url":"https://github.com/pyaf/load_forecasting","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pyaf%2Fload_forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pyaf%2Fload_forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pyaf%2Fload_forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pyaf%2Fload_forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pyaf","download_url":"https://codeload.github.com/pyaf/load_forecasting/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246558051,"owners_count":20796696,"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","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":["arima","electric-load-forecasting","gru","lstm","machine-learning","rnn","ses","sma","time-series-forecasting","wma"],"created_at":"2024-08-01T09:01:11.198Z","updated_at":"2025-04-02T05:31:12.685Z","avatar_url":"https://github.com/pyaf.png","language":"Jupyter Notebook","funding_links":[],"categories":["Code","Energy System Assessment","Energy Systems"],"sub_categories":["high citation paper (\u003e100)","Modeling","Load and Demand Forecasting"],"readme":"# Electric Load Forecasting\n\nUnder graduate project on short term electric load forecasting. Data was taken from [State Load Despatch Center, Delhi](www.delhisldc.org/) website and multiple time series algorithms were implemented during the course of the project.\n\n### Models implemented:\n\n`models` folder contains all the algorithms/models implemented during the course of the project:\n\n* Feed forward Neural Network [FFNN.ipynb](models/FFNN.ipynb)\n* Simple Moving Average [SMA.ipynb](models/SMA.ipynb)\n* Weighted Moving Average [WMA.ipynb](models/WMA.ipynb)\n* Simple Exponential Smoothing [SES.ipynb](models/SES.ipynb)\n* Holts Winters [HW.ipynb](models/HW.ipynb)\n* Autoregressive Integrated Moving Average [ARIMA.ipynb](models/ARIMA.ipynb)\n* Recurrent Neural Networks [RNN.ipynb](models/RNN.ipynb)\n* Long Short Term Memory cells [LSTM.ipynb](models/LSTM.ipynb)\n* Gated Recurrent Unit cells [GRU.ipynb](models/GRU.ipynb)\n\nscripts:\n\n* `aws_arima.py` fits ARIMA model on last one month's data and forecasts load for each day.\n* `aws_rnn.py` fits RNN, LSTM, GRU on last 2 month's data and forecasts load for each day.\n* `aws_smoothing.py` fits SES, SMA, WMA on last one month's data and forecasts load for each day.\n* `aws.py` a scheduler to run all above three scripts everyday 00:30 IST.\n* `pdq_search.py` for grid search of hyperparameters of ARIMA model on last one month's data.\n* `load_scrap.py` scraps day wise load data of Delhi from [SLDC](https://www.delhisldc.org/Loaddata.aspx?mode=17/01/2018) site and stores it in csv format.\n* `wheather_scrap.py` scraps day wise whether data of Delhi from [wunderground](https://www.wunderground.com/history/airport/VIDP/2017/8/1/DailyHistory.html) site and stores it in csv format.\n\n`server` folder contains django webserver code, developed to show the implemented algorithms and compare their performance. All the implemented algorithms are being used to forecast today's Delhi electricity load [here](http://forecast.energyandsystems.com) [now deprecated]. Project report can be found in [Report](Report) folder. \n\n![A screenshot of the website](screenshots/website.png \"A screenshot of the website\")\n\n\n### Team Members:\n\n* Ayush Kumar Goyal\n* Boragapu Sunil Kumar\n* Srimukha Paturi\n* Rishabh Agrahari\n\n## Star History\n\n[![Star History Chart](https://api.star-history.com/svg?repos=pyaf/load_forecasting\u0026type=Date)](https://star-history.com/#pyaf/load_forecasting\u0026Date)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyaf%2Fload_forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpyaf%2Fload_forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyaf%2Fload_forecasting/lists"}