Abstract
Since occupancy affects energy consumption, it is common to model and simulate occupancy using simulation software. One drawback of simulation software is that it cannot provide data transmission information from the sensors, which is essential for real-time energy monitoring systems. This paper proposes an approach to integrating an occupancy model and a real-time monitoring system for real-time modeling. The integration was performed by implementing a model on embedded devices and employing an IoT-based real-time monitoring application. The experimental results showed that the proposed approach effectively configured and monitored the model using a smartphone. Moreover, the data generated by the model were stored in an IoT cloud server for monitoring and further analysis. The evaluation result showed that the model ran perfectly in real-time embedded devices. The assessment of the IoT data transmission performances yielded a maximum latency of 9.0348 s, jitter of 0.9829 s, inter-arrival time of 5.5085 s, and packet loss of 10.8%, which are adequate for real-time modeling of occupancy-based energy consumption.
Highlights
Monitoring energy consumption in a building is an essential tool for improving energy efficiency [1]
In [3], the building energy consumption was divided into two categories: basic energy consumption that represents constant electrical usage such as emergency lamps and variable energy consumption that represents electrical usage affected by occupants such as indoor lighting, air conditioning (AC), and plug load
We developed the implementation of occupancy-based energy consumption in a campus building on embedded devices
Summary
Monitoring energy consumption in a building is an essential tool for improving energy efficiency [1]. We cannot use simulation software models as input data generators for testing real-time energy monitoring application systems. We propose a system to integrate the occupancy-based building energy consumption model with a real-time monitoring application to overcome the drawbacks mentioned above. The integration was achieved by implementing the occupancy-based building energy consumption model on an embedded device and employing an IoT-based real-time monitoring application. We developed a real-time application for energy monitoring and tested it using the proposed model. The sensors sense the environment and electrical parameters of the building, such as the room occupancy, the status of electrical appliances, the room temperature and humidity, and the status of windows and blinds opening These instruments send data to the cloud server using wireless or wired networks. The main benefit of our approach is that we may simulate several algorithms related to occupancy-based energy management with real-time interfacing to the existing IoT systems. We measure the data transmission parameters to evaluate the reliability of the proposed real-time simulator in a real-time environment
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