Dataset level explanation of heat demand forecasting ANN with SHAP

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

Energy, 2022, vol. 261, issue PA

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.

Keywords: Heat demand forecasting; Machine learning; Explainable AI; Shapley additive explanations; Contents (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222019703

DOI: 10.1016/j.energy.2022.125075

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