Ignatius Ginting, Melisa Mulyadi, Linda Wijayanti, Ferry Rippun Gideon Manalu

Pengembangan Perangkat Lunak Untuk Akuisisi Data Konsumsi Daya Listrik Dari Power Meter

Introduction

Pengembangan perangkat lunak untuk akuisisi data konsumsi daya listrik dari power meter. Kembangkan perangkat lunak akuisisi data konsumsi daya listrik real-time dari power meter menggunakan Python dan IoT via NodeMCU ESP8266. Pantau penggunaan listrik Anda dengan mudah.

Abstract

Technological developments make it possible to monitor electricity consumption in real-time via the internet. The system developed uses a power meter to collect data, Python to process data and IoT connectivity via NodeMCU ESP8266 to send data to the internet. Python is used so that measurement data can be processed and presented in an easy-to-understand format. The test results show that electrical data measurements can be displayed with an error ranging from 0.04% -11.56%. This is because there is a delay from the time the power meter is measured until the data is displayed on ThingSpeak.

Review

The submitted work, "Pengembangan Perangkat Lunak Untuk Akuisisi Data Konsumsi Daya Listrik Dari Power Meter," describes the development of a software system designed for real-time monitoring of electricity consumption. The abstract outlines a practical approach utilizing a power meter for data collection, Python for processing, and an IoT-enabled NodeMCU ESP8266 for internet connectivity, specifically sending data to ThingSpeak. This system aims to address the growing need for accessible energy monitoring, presenting complex electrical data in an easy-to-understand format for users. The proposed solution demonstrates several commendable aspects. The integration of readily available and cost-effective technologies like Python and the ESP8266 makes this project potentially reproducible and applicable in various contexts, including smart home and energy management systems. The focus on real-time data acquisition and internet connectivity aligns well with contemporary demands for remote monitoring and data-driven decision-making in energy usage. The explicit goal of presenting data in an easily digestible format is also a significant strength, enhancing the usability and practical value of the developed software. While the initiative is positive, the abstract highlights a critical area for further exploration: the reported error range of 0.04% - 11.56%. The explanation attributing this to a delay between measurement and display on ThingSpeak warrants deeper investigation. A variation of over 11% suggests a potential issue beyond simple latency, possibly indicating data loss, sampling inaccuracies, or synchronization problems that significantly impact the system's reliability for precise monitoring. Future work should meticulously analyze the root causes of this higher error margin, explore mitigation strategies, and provide a more detailed breakdown of performance under different operating conditions. Further details on the specific "easy-to-understand format" and potential real-world applications or comparative analysis with existing solutions would also enhance the paper's overall impact and contribution.

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