Exploring innovative ways to use data while safeguarding consumer privacy.
Grid operators are faced with the challenge of enabling the additional load and services on the grid. They must connect various actors while still providing stable infrastructure, which is essential for transitioning towards renewable energy and electrification.
Grid operators develop and manage energy grids. In this case, Alliander employs roughly 7,000 staff members who oversee the provision of electricity, gas, and heat to more than 3 million Dutch households and businesses via cables and pipelines. The organization oversees a network spanning over 90,000 km of electricity grid and 40,000 km of gas grid. The network is one of the most dependable and trustworthy in the world. Reliable insights into the consumer side of the electricity grid are vital for the daily activities of Alliander. Furthermore, these insights are also essential for innovation and experiments, enabling the discovery of new ways to design, manage, and optimize the grid.
Privacy restricts access to data
Accurate load predictions, consumer segmentation, and insights into user behavior are data-intensive processes. Various AI solutions can assist in electricity management. However, the required consumer data, including smart meter data (time series) and static properties (in tabular form), fall under the GDPR. This restricts the practical application of data essential for Alliander.
Following privacy laws, this type of data can only be retained for a two-year period. After that, this valuable information is lost, making it impossible to extract energy consumption data during, for example, the summer or periods of very low or high temperatures.
Synthesized data from BlueGen.ai allows data experts to work with data for research, analysis, and predictions. Without privacy risks!
To still achieve the required insights, Alliander aims to utilize BlueGen.ai’s synthetic data software. With this, Alliander can generate load curves from smart meters at 15-minute intervals over a whole year for approximately 3,500 consumers. For the validation of the synthetic data, a downstream utility use case, including customer segmentation, has been chosen. This synthetic data, with the assistance of machine learning, yields identical outcomes to real data in predicting transformer overloads. BlueGen.ai’s synthetic smart meter data ensures complete compliance with GDPR privacy regulations.
Combining data internally and externally for research and analysis
Exploring and developing new innovative activities for services
Safeguarding privacy
“BlueGen.ai was the first party to successfully generate synthetic annual profiles based on smart meter data, that are of high quality and sufficient granularity, enabling follow-up analyses such as grid calculations, load and congestion forecasting etc. This is a very interesting result, particularly for grid operators and energy companies, as smart meter data is subject to privacy regulations and cannot be used without consent.”