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RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values

Author

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  • Joohyun Jang

    (Department of AI and Big Data, Soonchunhyang University, Asan 31538, Republic of Korea
    These authors contributed equally to this work.)

  • Woonyoung Jeong

    (Department of AI and Big Data, Soonchunhyang University, Asan 31538, Republic of Korea
    These authors contributed equally to this work.)

  • Sangmin Kim

    (Department of AI and Big Data, Soonchunhyang University, Asan 31538, Republic of Korea)

  • Byeongcheon Lee

    (Department of AI and Big Data, Soonchunhyang University, Asan 31538, Republic of Korea)

  • Miyoung Lee

    (Department of Software, Sejong University, Seoul 05006, Republic of Korea)

  • Jihoon Moon

    (Department of AI and Big Data, Soonchunhyang University, Asan 31538, Republic of Korea)

Abstract

Accurate daily peak load forecasting (DPLF) is crucial for informed decision-making in energy management. Deep neural networks (DNNs) are particularly apt for DPLF because they can analyze multiple factors, such as timestamps, weather conditions, and historical electric loads. Interpretability of machine learning models is essential for ensuring stakeholders understand and trust the decision-making process. We proposed the RAID (robust and interpretable DPLF) model, which enhances DPLF accuracy by recognizing daily peak load patterns and building separate DNN models for each day of the week. This approach was accessible for energy providers with limited computational resources, as the DNN models could be configured without a graphics processing unit (GPU). We utilized scikit-learn’s MLPRegressor for streamlined implementation, Optuna for hyperparameter optimization, and the Shapley additive explanations (SHAP) method to ensure interpretability. Applied to a dataset from two commercial office buildings in Richland, Washington, RAID outperformed existing methods like recurrent neural networks, Cubist, and HYTREM, achieving the lowest mean absolute percentage error values: 14.67% for Building 1 and 12.74% for Building 2. The kernel SHAP method revealed the influence of the previous day’s peak load and temperature-related variables on the prediction. The RAID model substantially improved energy management through enhanced DPLF accuracy, outperforming competing methods, providing a GPU-free configuration, and ensuring interpretable decision-making, with the potential to influence energy providers’ choices and promote overall energy system sustainability.

Suggested Citation

  • Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6951-:d:1128502
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    References listed on IDEAS

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    1. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.

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