Indoor Air Quality Improvement

TL;DR

This paper presents an energy-efficient ventilation system that predicts occupancy patterns from historical data to enhance indoor air quality and reduce health risks.

Contribution

It introduces a novel predictive ventilation system that optimizes air quality by analyzing occupancy data, improving upon existing ventilation methods.

Findings

The system effectively predicts occupancy patterns.

It improves indoor air quality while reducing energy consumption.

The approach outperforms traditional ventilation control methods.

Abstract

Poor indoor air quality can contribute to the development of various chronic respiratory diseases such as asthma, heart disease, and lung cancer. Since air quality is extremely difficult for humans to detect though sensory processing, there is a need for efficient ventilation systems that can provide a healthier environment. In this paper, we have designed an energy efficient ventilation system that predicts sensor occupancy patterns based on historical data to improve indoor air quality.