In Europe, the building sector is responsible for 40% of energy consumption and 36% of greenhouse gas emissions. Improving energy efficiency in buildings is crucial to achieving sustainability goals. However, access to quality data for training machine learning models remains a significant obstacle. GDPR compliance and privacy concerns limit the availability of real-world energy datasets, posing challenges for researchers and innovators.

Synthetic data offers a groundbreaking solution by enabling the creation of realistic, privacy-preserving datasets. These simulated datasets can mimic energy consumption patterns in residential and commercial buildings, providing the foundation for developing innovative models and solutions to optimise energy usage.

Participants will work with a time series dataset representing building energy consumption. Generating reliable synthetic data enables secure, GDPR-compliant usage while preserving privacy.