This paper explores the application of Explainable AI (XAI) techniques to improve the transparency and understanding of predictive models in control of automated supply air temperature (ASAT) of Air Handling Unit (AHU). The study focuses on forecasting of ASAT using a linear regression with Huber loss. However, having only a control curve without semantic and/or physical explanation is often not enough. The present study employs one of the XAI methods: Shapley values, which allows to reveal the reasoning and highlight the contribution of each feature to the final ASAT forecast. In comparison to other XAI methods, Shapley values have solid mathematical background, resulting in interpretation transparency. The study demonstrates the contrastive explanations--slices, for each control value of ASAT, which makes it possible to give the client objective justifications for curve changes.

本研究解决了自动供应空气温度（ASAT）控制模型透明度不足的问题。通过采用可解释人工智能方法中的Shapley值，研究揭示了各特征对ASAT预测结果的贡献，提供了透明的数学解释。研究表明，这种方法能够为每个控制值提供对ASAT变化的客观 justification，具有显著的应用潜力。

基于可解释人工智能的供应空气温度预测系统