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Generic residential load profile generator based on weather data and occupancy

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  • Lekhel, Cheikh Elekbir Sidi
  • Mbayed, Rita
  • Velihorskyi, Oleksandr
  • Husev, Oleksandr
  • Monmasson, Eric

Abstract

Due to changing policies favoring renewable energy, residential energy management increasingly requires flexible consumption forecasting to optimize energy sources and costs. This paper introduces a simple application to generate home electricity consumption profiles by combining thermal and mathematical modeling with weather forecasts, occupancy schedules, and user settings. The model classifies household loads into thermostatically controlled appliances such as heat pump, water heater, and refrigerator and those highly depend on occupant behavior like lighting, washing machine, and common ON/OFF devices. By accounting seasonal changes, occupancy schedules, and varying temperatures, the model reflects real case conditions. Validation conducted under diverse conditions in a French context reveals that daily energy consumption can range from 14 kWh to 30 kWh, underscoring the adaptability of the proposed approach with different scenarios. A fully functional prototype, deployed on a Raspberry Pi 4 and integrated with Home Assistant, computes detailed 24-hour load forecasts with a resolution of one second. This modeling framework facilitates integration into home energy management systems or demand side management, offering a model with the ability to adjust for weekday or weekend schedules. Moreover, its generic design and flexible in changing parameters enable adaptation to different household sizes, number of occupants and insulation types, making it well suited to a wide range of residential scenarios.

Suggested Citation

  • Lekhel, Cheikh Elekbir Sidi & Mbayed, Rita & Velihorskyi, Oleksandr & Husev, Oleksandr & Monmasson, Eric, 2025. "Generic residential load profile generator based on weather data and occupancy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 237(C), pages 373-389.
    • Handle: RePEc:eee:matcom:v:237:y:2025:i:c:p:373-389
    DOI: 10.1016/j.matcom.2025.04.044
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    References listed on IDEAS

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    1. Wenzhi Cao & Houdun Liu & Xiangzhi Zhang & Yangyan Zeng & Xiao Ling, 2025. "Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors," Sustainability, MDPI, vol. 17(3), pages 1-21, January.
    2. Wang, Junke & Jiang, Yilin & Tang, Choon Yik & Song, Li, 2022. "Development and validation of a second-order thermal network model for residential buildings," Applied Energy, Elsevier, vol. 306(PB).
    3. Claeys, Robbert & Cleenwerck, Rémy & Knockaert, Jos & Desmet, Jan, 2023. "Stochastic generation of residential load profiles with realistic variability based on wavelet-decomposed smart meter data," Applied Energy, Elsevier, vol. 350(C).
    4. La Tona, G. & Luna, M. & Di Piazza, M.C., 2024. "Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 63-75.
    5. Oliveira Panão, Marta J.N. & Mateus, Nuno M. & Carrilho da Graça, G., 2019. "Measured and modeled performance of internal mass as a thermal energy battery for energy flexible residential buildings," Applied Energy, Elsevier, vol. 239(C), pages 252-267.
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