Explainable AI based System for Supply Air Temperature Forecast
This work addresses the need for interpretable predictive models in HVAC control systems, but it is incremental as it applies an existing XAI method to a specific domain.
The paper tackles the problem of forecasting automated supply air temperature in air handling units by applying Shapley values from Explainable AI to a linear regression model with Huber loss, resulting in transparent feature contributions and contrastive explanations for control curve changes.
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.