A Simple Real-Time Energy Analytics Model for Smart Building Using Open IoT Platforms

  Muhammad Nasar (1*), Novendra Setyawan (2), Amrul Faruq (3), Indah Sulistiyowati (4)

(1) Electrical Engineering Department - University of Muhammadiyah Malang - Indonesia
(2) Electrical Engineering Department - University of Muhammadiyah Malang - Indonesia
(3) Electrical Engineering Department - University of Muhammadiyah Malang - Indonesia
(4) Department of Electrical Engineering - University of Muhammadiyah Sidoarjo - Indonesia
(*) Corresponding Author

Received: November 22, 2019; Revised: December 16, 2019
Accepted: December 23, 2019; Published: December 31, 2019

How to cite (IEEE): M. Nasar, N. Setyawan, A. Faruq,  and I. Sulistiyowati, "A Simple Real-Time Energy Analytics Model for Smart Building Using Open IoT Platforms," Jurnal Elektronika dan Telekomunikasi, vol. 19, no. 2, pp. 83-90, Dec. 2019. doi: 10.14203/jet.v19.83-90


Monitoring energy in buildings can ease us to have a better understanding of electricity consumption patterns to support efficiency and avoid potential damages. However, indoor installations are mostly unmonitored because their panel meters are usually difficult to access. Yet, indoor maintenance tends to be more difficult since the cables are inside the wall, ceiling, or concrete. Internet of Things and big data analytics can be used to track electricity usage either in residential, commercial, or industrial buildings. This paper presents how a simple real-time energy data analytics was built at a low cost and high accuracy to inspect energy fluctuations, anomaly, and its significant pattern. We proposed 3 layers of architecture namely acquisition, transportation, and application management. An electronic module named PZEM004T was used to sense voltage, current, and other electrical parameters. Through a microcontroller ESP8266, the data was processed and sent it to an application layer via an existing wireless network. The actual and historical data of electricity were visualized on high-resolution graphs. The experiment was conducted at our office building. The experimental results showed that data of electrical energy usage can be captured close to realtime and power anomaly and pattern can be figured. Performance and functionality testing showed acceptable use of this system with more than 99% accuracy. This system is intended to empower building managers in evaluating the electrical network balance as well as anticipating damage due to overload, overvoltage, and voltage drop. If this model is widely implemented it will produce big data that is useful for advanced analysis.



Energy data analytics; energy management; Internet of Things; smart building

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