## Introduction and context
**Update:** winner announcement and final remarks were [posted in the discussion forum](https://www.kaggle.com/competitions/ai-based-modeling-for-energy-efficient-buildings/discussion/640972).
Modern commercial and industrial buildings increasingly rely on sophisticated Building Management Systems to coordinate their Heating, Ventilation, and Air Conditioning (HVAC). Such HVAC systems are very energy-intensive, often accounting for up to 50% of a typical facility's energy consumption. Efficient HVAC operation is therefore critical for achieving sustainability goals, reducing utility costs, and ensuring comfortable indoor environments. However, the inherent complexity of HVAC systems - which must respond dynamically to variations in weather, occupancy, equipment efficiency, and process requirements - makes them challenging to model and optimize manually.
Join this competition to build cutting-edge Machine Learning (ML) methods that learn models of such HVAC systems from measurement data of such buildings.
We collected a large dataset including hundreds of sensor time series over a period of 1.5 years, of a large real-world building - the [Bosch Budapest Campus](https://www.homeofbosch.com/en/projects/campus-with-rooftop-gardens/). The focus is on a chilled water plant that cools the building. In the current competition, the idea is to model the cooling load's relation to other factors, and predict ahead this load. Such models and predictions are an important ingredient to optimize the energy efficiency of such HVAC systems.
**Example code available:** A [GitHub repository is available with example code for this competition](https://github.com/boschresearch/elias_aifbo), containing simple, initial versions of the data loading/preprocessing/training/submission building blocks of a method for this competition. This allows a quick start. But note that all of the blocks may have to be adapted for the optimal method, in particular to load additional 2024 data.
**Stay tuned for a planned follow-up competition** that will focus on the optimization/control aspects, based on the current competition's models.
This competition is part of a project provided by [ELIAS - European Lighthouse of AI for Sustainability](https://elias-ai.eu/). This project has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement No. 101120237.
