This analysis demonstrates data anonymization and aggregation techniques in local energy communities, suggesting efficient sharing paths for stakeholders.
Key Points
The k-anonymity principle enhances data privacy by utilizing the Mondrian k-anonymization algorithm.
Data aggregation provides useful organization and representation of extracted data for local energy communities.
Self-Organizing maps generate compact models for data visualization, aiding stakeholders in analysis and inference.
Experimental results indicate the potential for implementing these approaches in energy data spaces for broader access.