Optimizing smart building energy management systems through industry 4.0: A response surface methodology approach

Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficien...

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Vydáno v:Green Technologies and Sustainability Ročník 2; číslo 2; s. 100079
Hlavní autoři: Seraj, Mohammad, Parvez, Mohd, Khan, Osama, Yahya, Zeinebou
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.05.2024
KeAi Communications Co., Ltd
Témata:
Energy
Energy-economic system
Industry 4.0
Response surface methodology
Sensors
Soft computing techniques
HLE
ANN
Energy-economic system
AI
Industry 4.0
Response surface methodology
RSM
VLE
IoT
ATC
Soft computing techniques
HVAC
Energy
RH
DBT
EPA
GA
ANFIS
Sensors
PM
MSE
ASHRAE
ISSN:2949-7361, 2949-7361
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Shrnutí:Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficient adjustment to internal room parameters and limited optimization based on varying loads. The current study investigates the influence of Industry 4.0 by assessing internal room parameters in an indoor ventilated room conditions for system output. Smart systems like AI and IoT, integrated with sensors, dynamically adjust indoor conditions based on external climate, enhancing HVAC system efficiency. Using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), integrated with AI and IoT sensors, optimally adjust indoor conditions in correlation with external climate, maximizing HVAC system efficiency. The study utilizes input parameters including Dry Bulb Temperature (DBT), Relative Humidity (RH), and Solar Radiation across diverse days to analyze system efficiency. ANN and RSM iteratively optimize system efficiency by training on RH, DBT, and air quality data. They converge on recommending 42% RH, 28 °C DBT, and 25 PM air quality, achieving 0.851 desirability. This approach enhances heat load (75.89%) and ventilation load (69.12%) efficiency significantly. Integration of Industry 4.0 sensors in the HVAC system resulted in a 16% efficiency increase and 15% cost effectiveness in the controlled room compared to prior measurements. •Industry 4.0 integration enhances HVAC efficiency via AI and IoT sensor-based adjustments.•ANN and RSM optimize indoor conditions using DBT, RH, and air quality data.•Recommended settings of 42% RH, 28°C DBT, and 25 PM air quality achieve high desirability.•Integration of Industry 4.0 sensors leads to a 16% efficiency increase and 15% cost effectiveness in controlled rooms.
ISSN:2949-7361
2949-7361
DOI:10.1016/j.grets.2024.100079