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Accurate energy demand forecasting in the building sector is essential for sustainable energy planning and policy development. This paper proposes a Genetic Algorithm GA based methodology for forecasting energy demand in Morocco’s building sector until 2050. The methodology takes into account historical energy consumption data, socioeconomic factors, and urbanization patterns to represent the complex processes that drive energy demand. The model is evaluated against real International Energy Agency data, indicating a strong correlation coefficient (R2 = 0.9634) between projected and observed energy demand. Once validated, the model uses predicted socioeconomic data to assess habitable areas and then anticipates future power usage for buildings. The results show that energy demand increased progressively during the simulation period, with significant results projected for 2030, 2040, and 2050. By 2030, energy demand is predicted to increase by 84.33% over 2017, due to growing populations and economic development. Energy demand is expected to increase by 74.27% and 79.50% in 2040 and 2050, respectively, reflecting the continued growth of energy needs. These findings highlight the significance of strategic energy planning and sustainable infrastructure development in Morocco’s building industry, which promotes resilience, efficiency, and environmental sustainability.

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Forecasting Energy Demand in the Building Sector up to 2050: A Genetic Algorithm Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Aboubekr Allam,
  • Mouad Karmoun,
  • Hassan Zahboune,
  • Mohamed Maaouane,
  • Smail Zouggar,
  • Mohamed Elhafyani,
  • Taoufik Ouchbel

摘要

Accurate energy demand forecasting in the building sector is essential for sustainable energy planning and policy development. This paper proposes a Genetic Algorithm GA based methodology for forecasting energy demand in Morocco’s building sector until 2050. The methodology takes into account historical energy consumption data, socioeconomic factors, and urbanization patterns to represent the complex processes that drive energy demand. The model is evaluated against real International Energy Agency data, indicating a strong correlation coefficient (R2 = 0.9634) between projected and observed energy demand. Once validated, the model uses predicted socioeconomic data to assess habitable areas and then anticipates future power usage for buildings. The results show that energy demand increased progressively during the simulation period, with significant results projected for 2030, 2040, and 2050. By 2030, energy demand is predicted to increase by 84.33% over 2017, due to growing populations and economic development. Energy demand is expected to increase by 74.27% and 79.50% in 2040 and 2050, respectively, reflecting the continued growth of energy needs. These findings highlight the significance of strategic energy planning and sustainable infrastructure development in Morocco’s building industry, which promotes resilience, efficiency, and environmental sustainability.