Reliable Integration of Neural Network and Internet of Things for Forecasting, Controlling, and Monitoring of Experimental Building Management System

In this paper, Internet of Things (IoT) and artificial intelligence (AI) are employed to solve the issue of energy consumption in a case study of an education laboratory. IoT enables deployment of AI approaches to establish smart systems and manage the sensor signals between different equipment base...

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Bibliographic Details
Published in:Sustainability Vol. 15; no. 3; p. 2168
Main Authors: Essa, Mohamed El-Sayed M., El-shafeey, Ahmed M., Omar, Amna Hassan, Fathi, Adel Essa, Maref, Ahmed Sabry Abo El, Lotfy, Joseph Victor W., El-Sayed, Mohamed Saleh
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2023
Subjects:
Analysis
Architectural design
Artificial intelligence
Buildings
Case studies
Control
Design and construction
Egypt
Energy conservation
Energy consumption
Engineering research
Environmental aspects
Intelligent buildings
Internet of Things
Neural networks
Sensors
Technology application
Egypt
ISSN:2071-1050, 2071-1050
Online Access:Get full text
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Summary:In this paper, Internet of Things (IoT) and artificial intelligence (AI) are employed to solve the issue of energy consumption in a case study of an education laboratory. IoT enables deployment of AI approaches to establish smart systems and manage the sensor signals between different equipment based on smart decisions. As a result, this paper introduces the design and investigation of an experimental building management system (BMS)-based IoT approach to monitor status of sensors and control operation of loads to reduce energy consumption. The proposed BMS is built on integration between a programmable logic controller (PLC), a Node MCU ESP8266, and an Arduino Mega 2560 to perform the roles of transferring and processing data as well as decision-making. The system employs a variety of sensors, including a DHT11 sensor, an IR sensor, a smoke sensor, and an ultrasonic sensor. The collected IoT data from temperature sensors are used to build an artificial neural network (ANN) model to forecast the temperature inside the laboratory. The proposed IoT platform is created by the ThingSpeak platform, the Bylink dashboard, and a mobile application. The experimental results show that the experimental BMS can monitor the sensor data and publish the data on different IoT platforms. In addition, the results demonstrate that operation of the air-conditioning, lighting, firefighting, and ventilation systems could be optimally monitored and managed for a smart system with an architectural design. Furthermore, the results prove that the ANN model can perform a distinct temperature forecasting process based on IoT data.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su15032168