Fairness-aware data-driven-based model predictive controller: A study on thermal energy storage in a residential building

Abstract

Besides accuracy, fairness has been reported as another performance criterion for data-driven building models (DDBMs). To ensure data-driven-based model predictive controllers (MPCs) provide optimal control strategies based on fair or unbiased prediction, this study proposes the concept of fairness-aware data-driven-based MPC. In the proposed MPC framework, fairness-aware DDBMs constitute the prediction component that provides predicted building states to the objective function in the optimization part. To investigate the effect of improving fairness on the control performance, the proposed MPC is implemented in a residential building heated by an electrically heated floor (EHF) system, which could be considered as a thermal energy storage (TES) system. In the case study, fairness-aware DDBMs are developed to predict energy demand and indoor air temperature. Then, the predicted values are used to formulate the objective function to optimize the hourly day-ahead set-point temperature with the aim of minimizing the heating cost or maximizing peak shifting, while maintaining thermal comfort. The numerical study results show that although considering fairness improvement methods in DDBMs decreases the overall predictive accuracy, it provides fair prediction by narrowing the accuracy difference between majority conditions and minority conditions. For instance, a fairness-aware energy prediction model increases the overall mean absolute error (MAE) from 6.12 kWh to 7.56 kWh but decreases the MAE difference between a majority condition and a minority condition from 0.82 kWh to 0.14 kWh. Although improving predictive fairness comes with a price of overall predictive accuracy, fairness-aware data-driven-based MPCs show comparable peak shifting or cost-saving ability with the traditional MPC in which fairness is not considered. This study provides a reference for stakeholders to design and implement trustworthy MPCs in buildings based on fair and accurate prediction.