Enhancing Privacy Through Time Aggregation of Load Profiles in Energy Management

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Demand side management (DSM) applications rely on the exchange of load profiles to effectively manage the operation of energy systems. However, sharing detailed energy profile data raises substantial privacy concerns, such as potential misuse of personal information. To mitigate these concerns, we investigate the potential of time aggregation (TA), which involves merging multiple samples of a profile into a single value representing multiple intervals. TA reduces data exchange and computational requirements in energy management and helps preserve user privacy by reducing granularity of user data. We show that, for an effective implementation of TA in energy management, it is important to make the right choice of TA method. We compare and evaluate seven different TA methods. Furthermore, we perform TA across various time frames using an optimization based DSM approach. Our findings reveal that if we aggregate load profiles from 15 minutes to 4 hours, we obtain both enhanced privacy and a 21% decrease in the required number of iterations with the investigated DSM method, albeit at the cost of a 15% decrease in objective value performance. Based on this, we conclude that depending on the application needs, TA with a carefully selected aggregation method has the potential to bring value to energy management, even when aggregating to a considerable extent.
Original languageEnglish
Title of host publication2024 IEEE 8th Energy Conference (ENERGYCON)
Number of pages6
ISBN (Electronic)979-8-3503-8215-0
ISBN (Print)979-8-3503-8216-7
Publication statusPublished - 15 Apr 2024
EventIEEE 8th Energy Conference, ENERGYCON 2024 - Doha, Qatar
Duration: 4 Mar 20247 Mar 2024
Conference number: 8


ConferenceIEEE 8th Energy Conference, ENERGYCON 2024
Abbreviated titleENERGYCON 2024


  • Load profiles
  • Energy management
  • Demand side management
  • Time series aggegation
  • Privacy


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