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Electricity Energy Monitoring System for Home Appliances Using Raspberry Pi and Node-Red

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Soft Computing in Data Science (SCDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1771))

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Abstract

This paper research deals with the shortcoming of late electricity bills that are generated only after the electrician comes to the residential area and measures the reading of the electricity meter manually. Electricity bill is only submitted once a month in Malaysia. The main concern of the project is to design an Electricity Energy Monitoring System for Home Appliances using Raspberry Pi and Node-RED; consequently, to generate real-time bill. The architecture of the mechanism is designed on computer, using Node-RED-based coding and Circuito.Io-based simulation to achieve the goals of the project. The circuit design had been simulated before the real system was realized. Node-RED programming tool requires necessary coding to be keyed in into the Raspberry Pi. The microcontroller essentially provides the same function as NodeMCU, but is replaced with Arduino Uno as a more suitable choice, due to issues of the initial components having produced undesirable result. The outcome of this project is there are lots of information provided in dashboard form. The collected and generated data are stored in the SQLite database in Raspberry Pi. This project applies the Internet of Things (IoT) system to remotely monitor home appliances over the internet. In the proposed project, a current sensor senses the electricity energy, which then uploads the data via MQTT. The total consumption can be viewed using Node-RED.

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Acknowledgment

The authors would like to thank the Ministry of Education (MoE) and Management and Science University (MSU) for sponsoring this work under Grant ID no. SG-017-012020-FISE.

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Correspondence to Nur Shazwany Zamani .

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Zamani, N.S., Ramli, M.S.M., Yusof, K.H., Sapari, N.M. (2023). Electricity Energy Monitoring System for Home Appliances Using Raspberry Pi and Node-Red. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_10

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  • DOI: https://doi.org/10.1007/978-981-99-0405-1_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0404-4

  • Online ISBN: 978-981-99-0405-1

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