Explainable AI based System for Supply Air Temperature Forecast

TL;DR

This paper applies Shapley value-based Explainable AI techniques to enhance transparency in forecasting supply air temperature in HVAC systems, providing objective justifications for control curve changes.

Contribution

It introduces the use of Shapley values for interpretability in supply air temperature forecasting, improving understanding of feature contributions.

Findings

Shapley values effectively reveal feature contributions in ASAT prediction.

Contrastive explanations help justify control curve variations.

The approach enhances transparency in HVAC temperature control models.

Abstract

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.