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<p>Accurate prediction of domestic electricity consumption, particularly in intelligent buildings, is crucial for optimal resource utilization and allowing data-driven energy management. The comparison of Long Short-Term Memory (LSTM), Seq2Seq, and Prophet time-series models for electricity consumption prediction using the Low Carbon London dataset is carried out in this study. Preprocessing tasks involving the removal of outliers and missing value replacement were conducted, and Optuna was utilized to optimize model performance by tuning hyperparameters. Evaluation reveals that the Prophet model did the best in accuracy, then the LSTM model, which was remarkably improved via hyperparameter tuning, followed by Seq2Seq, which improved but performed slightly less effectively than LSTM. This article demonstrates the capability of deep learning models to recognize complex temporal patterns and provides a foundation for scalable, data-driven solutions in sustainable energy management. The study also sets the stage for future research on household-specific predictions and cluster-based optimization.</p>

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Electricity consumption prediction in smart buildings using deep learning approaches (2025)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Sufiyan Ul Rehman,
  • Nasir Iqbal

摘要

Accurate prediction of domestic electricity consumption, particularly in intelligent buildings, is crucial for optimal resource utilization and allowing data-driven energy management. The comparison of Long Short-Term Memory (LSTM), Seq2Seq, and Prophet time-series models for electricity consumption prediction using the Low Carbon London dataset is carried out in this study. Preprocessing tasks involving the removal of outliers and missing value replacement were conducted, and Optuna was utilized to optimize model performance by tuning hyperparameters. Evaluation reveals that the Prophet model did the best in accuracy, then the LSTM model, which was remarkably improved via hyperparameter tuning, followed by Seq2Seq, which improved but performed slightly less effectively than LSTM. This article demonstrates the capability of deep learning models to recognize complex temporal patterns and provides a foundation for scalable, data-driven solutions in sustainable energy management. The study also sets the stage for future research on household-specific predictions and cluster-based optimization.