Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance

TL;DR

This paper investigates how personal environmental control systems can improve HVAC performance robustness against occupancy prediction errors, demonstrating that PECs help maintain comfort and efficiency despite forecast inaccuracies.

Contribution

It introduces a systematic analysis of PECs' role in mitigating occupancy prediction errors' impact on MPC-based HVAC control.

Findings

Occupancy prediction errors degrade MPC HVAC performance.

PECs significantly improve robustness to forecast errors.

MPC with PECs outperforms simple static schedules under prediction inaccuracies.

Abstract

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.