An increasing number of distributed energy resources (DERs) introduces challenges to the energy system, e.g., grid balancing. This challenge is especially crucial to distribution system operators (DSOs) who maintain control of the energy grid. Compared to the large-scale battery, demand response is a better solution. However, demand response might create enormous activities in the grid due to the communication between the electricity markets and the demand sides, which potentially affects the distribution grid stability. Therefore, accurate predictions of the future loads in the distribution grid are necessary. Globally, households make up a large part of electricity. However, most distribution grid designs cannot afford a large number of DERs, especially synchronous charging. Thus, it is critical for DSOs to accurately forecast energy load in the residential areas to improve their operation quality and reduce maintenance costs. 

This competition is hosted as a Test for future competitions.
###Problem formulation
In this competition, you will be developing machine learning models, that can accurately forecast electricity load for Danish residential areas.