# 👋 Introduction 

![Alt Text](https://media.giphy.com/media/nRk3z3sg6Ano6Y7gX2/giphy.gif)

The decentralized expansion of renewable energies is an important pillar on the way to the energy transition. Self-generation systems on industrial and commercial sites are a major field of application. Primarily, customers consume the electricity they generate themselves and purchase electricity if the quantities generated are insufficient to completely cover their energy load. The resulting residual load (residual load = energy load - self generated energy) must in any case be provided by the energy supplier (aka. the grid). Note, that the residual load can also be negative if the amount of self generated energy is greater than the load. In this case, the excess energy is fed into the electricity grid.

In order to ensure a stable energy supply, the energy suppliers rely on forecasts of these residual loads. In the past, those residual loads could be forecast based on many years of experience. Now, the share of solar energy systems and therefore the amount of energy self-generation increases more and more. This makes forecasting the residual loads more and more complex as there are dependencies on external factors like the weather. However, the forecast is necessary to maintain a proper supply operation. For this reason, it is essential to have a forecast of the residual load that is as accurate as possible.

# 🚀Problem Statement

Participants are asked to develop an algorithm that forecasts the residual loads. You can find more technical problem descriptions and the data in the [data tab](https://www.kaggle.com/competitions/energy-forecasting-data-challenge/data).

# Organizers

|![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1811734%2F9253fffaf8bf37bba5b85c3e93f813b6%2Fhessian_ai.png?generation=1669728657947391&alt=media)   | ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1811734%2F48e1cafdec6ab6371820f7e7650de5db%2Fhoe.jpg?generation=1669728658280042&alt=media)   | ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1811734%2F8761c0dff398f0b9859f2b027216e1fd%2Faisr.png?generation=1669728658607042&alt=media)   | ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1811734%2F7e8796d39ca2b60ab0a798ff6a3483d3%2FSTW_AG_4c.jpg?generation=1669728658510958&alt=media)  |
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| [hessian.AI](https://hessian.ai/)| [House of Energy](https://www.house-of-energy.org/) | [AI Startup Rising](https://hessian.ai/entrepreneurship/)| [Städtische Werke AG, Kassel](https://www.sw-kassel.de/privatkunden/startseite/)|)