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.

翻译：本文探讨了可解释人工智能（XAI）技术在提升空气处理机组（AHU）自动送风温度（ASAT）控制中预测模型透明度与可理解性方面的应用。研究聚焦于使用带Huber损失的线性回归模型进行ASAT预测。然而，仅凭缺乏语义和/或物理解释的控制曲线往往是不够的。本研究采用了一种XAI方法——沙普利值，该方法能够揭示推理过程并突出每个特征对最终ASAT预测的贡献。相较于其他XAI方法，沙普利值具有坚实的数学基础，从而保证了解释的透明度。研究展示了针对ASAT各控制值的对比解释——数据切片，这使得向客户提供曲线变化的客观依据成为可能。