住宅用电量监测的智能电表设计

Muhammad Mansattha, H. Dao, Arfip Jikaraji
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引用次数: 0

摘要

电力消费是经济发展的一个关键问题,特别是在住宅领域。尽管能源供应不断减少,家庭数量却显著增加。显然需要一个有效的电力消耗监测系统,能够提供准确的住宅能源消耗数据。本文提出了一种基于物联网(IoT)的智能电表系统设计,用于家庭能耗监测。该系统采用硬件和物联网技术,特别是ESP-32的Node-MCU和一个运行在5(15)安培单相电表上的ADE7757功率传感器模块。能耗数据通过Google cloud上托管的AppSheet平台登录到云存储中。该系统具有数字显示和消费分析功能,允许消费者收集和传输有关其能源使用的数据。能源使用数据为消费者提供准确、及时的能源消耗信息。这些信息可以帮助他们更好地管理和减少他们的能源使用。该系统还为能源部门提供了一个用户友好的监控体验,为单个电器提供能源消耗的估计。所提出的智能电表系统已经使用1,080个数据集进行了评估,与5(15)安培的单相电表相比,平均准确率为1.48%。此外,该系统还可以根据泰国省电力局(PEA)规定的居民电价表预测能源费用,准确率为0.02%。这些结果表明,该系统具有很高的准确性,可以促进积极的用户行为,以更好地进行能源供需管理,减少能源浪费,提高系统可靠性。智能电表系统的特点使消费者和公用事业公司能够在能源使用方面做出更明智的决定,促进更高效和可持续的能源实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart meter design for energy consumption monitoring of residential premises
Electricity consumption is a crucial issue for economic development, especially in the residential sector. The number of households has increased significantly despite the declining availability of energy resources. There is a clear need for an efficient electricity consumption monitoring system that can provide accurate data on residential energy consumption. This paper proposes an Internet of Things (IoT)-based smart meter system design for monitoring household energy consumption. The system employs hardware and IoT technology, specifically the Node-MCU of ESP-32 and an ADE7757 power sensor module which runs on a 5(15) Amp of a single-phase meter. The energy consumption data is logged in cloud storage using the AppSheet platform hosted on Google Cloud. The system features digital displays and consumption analytics, allowing consumers to collect and transmit data about their energy usage. Energy usage data provides consumers accurate and timely information about their energy consumption. This information can help them better manage and reduce their energy usage. The system also offers the estimation of the energy consumption for individual appliances with a user-friendly monitoring experience for the energy sector. The proposed smart meter system has been evaluated using 1,080 data sets, with an average accuracy rate of 1.48% compared to a 5(15) Amp, single-phase meter. Additionally, the system can predict energy charges with an accuracy of 0.02% based on the schedule of residential electricity tariff regulated by the Provincial Electricity Agency (PEA), Thailand. These results show that the system is highly accurate and can promote positive user behavior towards better energy supply and demand management, reduced energy waste, and improved system reliability. The features of smart meter systems enable consumers and utilities to make more informed decisions about energy usage, promoting more efficient and sustainable energy practices.
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