1 year ago
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Sustainability, Vol. 15, Pages 2168: Reliable Integration of Neural Network and Internet of Things for Forecasting, Controlling, and Monitoring of Experimental Building Management System
Sustainability doi: 10.3390/su15032168
Authors: Mohamed El-Sayed M. Essa Ahmed M. El-shafeey Amna Hassan Omar Adel Essa Fathi Ahmed Sabry Abo El Maref Joseph Victor W. Lotfy Mohamed Saleh El-Sayed
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.
