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<p>Forecasting power demand is crucial for developing countries like Bangladesh for various reasons including resource planning due to limited resources. Limited research was found on short-term power demand forecasting of Bangladesh. In this study, a preprocessing pipeline is proposed to generate powerful features including hourly demand, weather and economic data to generate both short- and medium-term load forecasting. Our method achieved the lowest 2.3% MAPE on PGCB dataset in forecasting energy loads for January and February 2024. The efficacy of the generated features, produced from pre-processing pipeline, was validated by utilising 2 machine-learning models including FB-Prophet and LSTM.</p>

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Enhanced power demand forecasting for Bangladesh: using feature engineering associated with environmental and economic impact

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月27日

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  • Muhammad Tanveer Islam,
  • Sartaj Aziz Turja,
  • Ahsan Habib

摘要

Forecasting power demand is crucial for developing countries like Bangladesh for various reasons including resource planning due to limited resources. Limited research was found on short-term power demand forecasting of Bangladesh. In this study, a preprocessing pipeline is proposed to generate powerful features including hourly demand, weather and economic data to generate both short- and medium-term load forecasting. Our method achieved the lowest 2.3% MAPE on PGCB dataset in forecasting energy loads for January and February 2024. The efficacy of the generated features, produced from pre-processing pipeline, was validated by utilising 2 machine-learning models including FB-Prophet and LSTM.