用可解释AI提升空调送风温度预测的透明度与可信度
Explainable AI based System for Supply Air Temperature Forecast
- 采用带Huber损失的线性回归预测送风温度
- 通过Shapley值揭示各特征对预测结果的贡献度
- 生成对比解释切片，支持运维人员理解控制曲线变化
本文研究了可解释人工智能（XAI）技术在空调机组（AHU）送风温度（ASAT）自动控制中的应用，旨在提升预测模型的透明性与可理解性。研究基于带Huber损失的线性回归方法进行ASAT预测，但仅提供控制曲线缺乏语义或物理解释常难以被接受。为此，本文采用Shapley值这一具有坚实数学基础的XAI方法，量化各输入特征对最终预测结果的贡献，实现决策过程的可解释性。研究进一步提出针对每个控制值的对比解释切片，使系统能够为送风温度曲线的调整提供客观、可追溯的理由，增强用户信任与运维效率。
原文摘要 · Abstract (English)
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.
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