A Novel Unified Environmental Quality Control Index based on AI Towards Smart building Optimization

Abstract

Assessment and monitoring of health and working conditions in the workplace is an important issue. Human health, safety and productivity are not only greatly affected by health and working conditions, but the equipment, machinery and materials in those environments can be affected in ways that lead to degradation. This paper presents a way to use artificial intelligence in developing a novel entity index for assessing and monitoring workplace health and conditions of use in intelligent buildings. Based on fuzzy logic, two algorithms were developed to determine the relationships and dependencies between various immediate environmental indicators and underlying environmental variables, to account for these relationships and trends, and finally to represent the indicator values for temperature, health and working conditions. A table was developed with temperature ranges and the effects on occupancy experience within those ranges. The current environmental indicators used in the previously unambiguous algorithm to generate new index values are apparent temperature (air cooling coefficient, wet bulb temperature and heat index), temperature and humidity index, discomfort index, warmth, comfort, and heat capacity. Based on Fuzzy logic, the environment variables of the algorithm are ambient temperature, relative humidity, and air velocity. After developing a complete system model using MATLAB/Simulink, further testing and evaluating the algorithm design, a model was created containing all indexed sub models, fuzzy sub model algorithms, input blocks, and data visualizations.