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Dataset level explanation of heat demand forecasting ANN with SHAP

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  • Białek, Jakub
  • Bujalski, Wojciech
  • Wojdan, Konrad
  • Guzek, Michał
  • Kurek, Teresa

Abstract

This paper aims to provide a thorough guide on how to analyze complex energy demand forecasting models with Shapley Additive exPlanations (SHAP) in order to build trust in their predictions and understand the model and SHAP limitations based on selected real-world use case. There are only few examples described in the literature in energy industry and they present very basic usage. This study fills the gap for the class of energy (heat, electric, gas) demand predicting models by showing step by step, top-down analysis of state-of-the-art, deep neural network model predicting total heat demand (hot water and room heating) in Warsaw District Heating Network – the largest district heating network in EU. The paper shows how SHAP can be successfully used on demand forecasting models to provide practical, easily interpretable insights on inner workings of these models which can be used to assess their reliability and plan further development.

Suggested Citation

  • Białek, Jakub & Bujalski, Wojciech & Wojdan, Konrad & Guzek, Michał & Kurek, Teresa, 2022. "Dataset level explanation of heat demand forecasting ANN with SHAP," Energy, Elsevier, vol. 261(PA).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222019703
    DOI: 10.1016/j.energy.2022.125075
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    1. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
    3. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
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    Cited by:

    1. Lei, Yang & Chen, Yuming & Chen, Jinghai & Liu, Xinyan & Wu, Xiaoqin & Chen, Yuqiu, 2023. "A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas," Energy, Elsevier, vol. 273(C).
    2. van Zyl, Corne & Ye, Xianming & Naidoo, Raj, 2024. "Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP," Applied Energy, Elsevier, vol. 353(PA).

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