{"id":19771988,"url":"https://github.com/danieldacosta/energy-forecast","last_synced_at":"2025-04-30T17:33:01.395Z","repository":{"id":43651951,"uuid":"279457616","full_name":"DanielDaCosta/energy-forecast","owner":"DanielDaCosta","description":"The repository consists of an energy forecasting model using XGboost. 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The dataset consists of hourly energy consumption rates in kWh for an industrial utility over a period of around 7 months, from July 2019 to January 2020.\n\nThe final model has an *forecasting horizon* (The number of time periods to forecast into the future) of 48 time periods which corresponds to 2 days ahead forecasting. \n\n# Details\nMain techniques and terms used:\n- Trend, Seasonality\n- Stationary and Non-Stationary Time Series\n- Augmented Dickey-Fuller Test\n- Autocorrelation(ACF) and Partial Autocorrelation(PACF)\n- Feature Engineering (lag features, standard time series features, endogenous features)\n- XGBoost model with Bayesan hyperparameters optimization\n\nThe final model can be found inside ```model/```\n\n# Usage\nThe Jupyter-Notebook, dataset and model were saved as Docker Image in GitHub Package.\nYou can install the package locally:\n```\ndocker pull docker.pkg.github.com/danieldacosta/energy-forecast/energy-forecast_notebook:v1\n```\n\nYou can also download it direct from the repository:\n\n```\ngit clone https://github.com/DanielDaCosta/energy-forecast.git\n```\n\n\n# Acknowlegments \u0026 References\nSpecial thanks to Mario Dagrada [Medium post](https://towardsdatascience.com/ml-time-series-forecasting-the-right-way-cbf3678845ff).\n\n**References**:\n- https://www.youtube.com/watch?v=Nm7m92sZZJA\n- https://towardsdatascience.com/containerize-your-whole-data-science-environment-or-anything-you-want-with-docker-compose-e962b8ce8ce5\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanieldacosta%2Fenergy-forecast","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanieldacosta%2Fenergy-forecast","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanieldacosta%2Fenergy-forecast/lists"}