Energy-focused predictive control for particulate matter concentration and thermal comfort indoors in Delhi

This paper presents a novel, model-based approach for energy-focused indoor particulate matter (PM) concentration control and indoor temperature management for a residential house. A physics-based model is developed to predict PM concentration and temperature indoors as a function of outdoor PM concentration, outdoor temperature, indoor emission, indoor heat generation, and flow rates of air handling unit (AHU), portable air filter (PAF), and air conditioning (AC) systems. The optimized settings of the ventilation systems AHU, PAF, and AC are determined employing a nonlinear model predictive control (MPC) algorithm. A case study in Delhi, one of the most polluted cities in the world, is conducted. Extensive simulation studies under seasonal changes in outdoor conditions and varying indoor emissions demonstrate the ability of the MPC controller to maintain PM concentration and temperature indoors within or close to the desired levels, while optimizing energy consumption.

Automated summary

This paper presents a novel, model-based approach for controlling indoor particulate matter (PM) concentration and temperature in a residential house. A physics-based model is developed to predict indoor PM concentration and temperature based on various factors. The optimized settings of ventilation systems are determined using a nonlinear model predictive control algorithm. A case study in Delhi demonstrates the ability of the control system to maintain desired indoor levels while optimizing energy consumption.