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Stochastic Markov-Based Modelling of Residential Lighting Demand in Luxembourg: Integrating Occupant Behavior and Energy Efficiency

Author

Listed:
  • Vahid Arabzadeh

    (Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1511 Luxembourg, Luxembourg)

  • Raphael Frank

    (Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1511 Luxembourg, Luxembourg)

Abstract

This study presents a stochastic Markov-based modeling framework for occupant behavior and residential lighting demand in Luxembourg. Integrating demographic data, time-use surveys, Markov chains, and dual-layer optimization, the model enhances the accuracy of non-HVAC energy demand simulations. The Harmonized European Time Use Surveys (HETUS) provide a detailed activity-based energy modeling approach, while Bayesian and constraint-based optimization improve data calibration and reduce modeling uncertainties. A Luxembourg-specific stochastic load profile generator links occupant activities to energy loads, incorporating occupancy patterns and daylight illuminance calculations. This study quantifies lighting demand variations across household types, validating results against empirical TUS data with a low mean squared error (MSE) and a minor deviation of +3.42% from EU residential lighting demand standards. Findings show that activity-aware dimming can reduce lighting demand by 30%, while price-based dimming achieves a 21.60% reduction in power demand. The proposed approach provides data-driven insights for energy-efficient residential lighting management, supporting sustainable energy policies and household-level optimization.

Suggested Citation

  • Vahid Arabzadeh & Raphael Frank, 2025. "Stochastic Markov-Based Modelling of Residential Lighting Demand in Luxembourg: Integrating Occupant Behavior and Energy Efficiency," Energies, MDPI, vol. 18(19), pages 1-34, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5133-:d:1759290
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    References listed on IDEAS

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