This paper presents an optimization algorithm for residential demand response that integrates user behavior into day-ahead load scheduling using Harmonized European Time Use Survey (HETUS) data. By balancing dynamic pricing with user preferences, the approach achieves electricity cost savings of up to 50% while minimizing disruptions to household routines. Results demonstrate the potential of personalized energy management to enhance user participation and improve residential demand response effectiveness.

Authors: Reda El Makroum, Sebastian Zwickl-Bernhard, Lukas Kranzl, Hans Auer

The paper was published in November 2025.