Using personal environmental comfort systems to mitigate the impact of occupancy prediction errors on HVAC performance

Energy Informatics

Milan Jain¹  Rachel K. Kalaimani²  Catherine Rosenberg³  Srinivasan Keshav³ 

[1] IIIT-Delhi, Delhi, India;IIT Madras, Tamil Nadu, India;University of Waterloo, Ontario, Canada

关键词: HVAC;    Control;    Optimisation;    Error analysis;    Thermal modelling;    Simulation;   

DOI  :  10.1186/s42162-018-0064-9

学科分类：计算机网络和通讯

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY