Buildings account for a significant portion of global energy consumption and reducing energy use in buildings is critical to meeting global emissions reduction targets.
Building energy load disaggregation involves identifying individual appliances or devices that consume energy within a building, which is essential for implementing targeted energy efficiency measures. The accurate disaggregation of individual appliance energy consumption from the total building energy consumption is a complex problem and has important implications for building owners and operators, energy providers, and consumers. Understanding how much energy individual appliances consume can enable building owners and operators to optimize energy consumption, reduce energy costs, and improve overall energy efficiency.
The energy load disaggregation problem has been the focus of research in the energy sector for many years, and it remains a challenging problem due to the complex and diverse nature of appliances and their energy usage patterns. Traditional methods for load disaggregation rely on installing sub-meters for each appliance, but this approach is costly and impractical for large-scale applications.
The Building Energy Load Disaggregation Challenge aims to develop accurate and scalable algorithms for disaggregating weather-dependent building energy use. The main goal of the competition is to drive innovation and progress in the field of building energy management, which can lead to significant energy savings and reductions in greenhouse gas emissions.
The accurate disaggregation of weather-dependent building energy use from the total building energy consumption is a complex problem that requires the development of accurate, efficient and scalable methods. This competition seeks to address this challenge by promoting the development of innovative solutions to the energy load disaggregation problem, enabling building owners and operators to optimize energy consumption, reduce energy costs, and improve overall energy efficiency.
The competition will challenge participants to develop efficient, effective, and scalable algorithms that can accurately identify energy use related to heating/cooling from the total building energy consumption, while taking the following key challenges into account:
Access to the dataset will be shared here, once it is available.
The usage of external data is allowed in this competition, as long as it is free and publicly available. The competitors must ensure that all data they use is freely available to all participants, and post access to the dataset on the competition forum before the end of the competition.
The competition will use result submission. Participants are required to locally compute the predictions and format it as a CSV file. The file needs to be zipped for submission and uploaded through the competition’s Codalab page, under the participate tab.
An example submission file will be available along with the dataset. This will show the exact format necessary for the submission.
The competition will use Mean Absolute Error (MAE) as the main metric of the leaderboard, and to determine the winners. Additionally, the leaderboard will also show Relative Mean Absolute Error (RMAE), which is a normalized version of MAE, for easier interpretation, and Mean Absolute Percentage Error (MAPE), which expresses the average deviation relative to the truth. They are calculated as follows:
Where ´y´ is the ground truth, ´x´ is the predicted value, and ´n´ is the number of time instances.
The competition will have three phases:
The Dates will be announced in the third quarter of 2023!
In this phase, a and participants will be given a chance to familiarize themselves with the competition environment, training data, and documentation, and also allow the raising of issues. There will be a public leaderboard, where the scoring will be done against the training data. We will also create a solution example, and provide baseline models.
This phase marks the beginning of the entry evaluation and ranking of submitted entries against hidden. A public leaderboard will be available where participants will see how their submissions rank against each other and how their latest submission ranks against other participants' submissions. At the beginning of this phase, the complete training dataset will be released.
New submissions will be halted at the beginning of this phase. The submitted entries will be evaluated against a separate hidden dataset, on a private leaderboard. The final leaderboard and selected winners will be published.
To ensure that the competition is conducted in a fair and ethical manner, and that all participants should hold to a high standard of conduct:
Participants can get in touch wit the organizers, and each other using the discussion forum on the competition’s Codalab page. The forum can be found at the following location: https://codalab.lisn.upsaclay.fr/forums/13292/
Pleas do not hesitate to contact us.
The prizes of this competition are sponsored by the Danish Energy Technology Development and Demonstration Programme (EUDP) fund
SDU Center for Energy Informatics, Denmark