Indoor Air Quality Control Using Backpropagated Neural Networks
Source abstract
Abstract
Providing comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and analyze model results by applying the proposed system. The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room. The proposed developed model takes into consideration international Heating, Refrigerating, and air conditioning standards as ASHRAE AND ISO. Based on the findings, we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Building Energy and Comfort Optimization · Air Quality Monitoring and Forecasting · Greenhouse Technology and Climate Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
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