Nowadays, applications of AI and ML are everywhere, spanning various industries from healthcare and finance to insurance, households (IoT), and energy applications. The training of AI models is inherently reliant on extensive datasets. In the context of the energy industry, crucial datasets, such as smart meter data, play a significant role in the development of consumption forecasts, demand response models, and retail pricing modeling. Software companies, utilities, traders, research institutions and TSOs/DSOs are interested in obtaining realistic datasets for developing such models. Nevertheless, privacy laws, such as GDPR, impose restrictions on data exchange. The process of data anonymization and the creation of realistic, yet artificial, datasets for training AI models is a complex and time-consuming task. Moreover, there is a risk of losing correlations if not executed professionally, and immature methodologies may result in the high re-identification quote of individual data from anonymized datasets. BlueGen’s software presents a solution to this challenging task. The methodology, derived from collaborative research with Technical University of Delft is scientifically proven. The company employs AI models to learn from real samples and create datasets with precisely the same behavior, ensuring that statistical properties of the original dataset are retained. These synthetic datasets can be… continue reading
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