AI-DRIVEN SMART HOME ENERGY MANAGEMENT AND CLOUD MONITORING SYSTEM USING NODEMCU AND PZEM-004T

Abstract

The increasing demand for electricity and the rapid growth of residential energy consumption have created the need for intelligent home energy management systems that improve energy efficiency while reducing electricity costs. Conventional energy monitoring systems provide limited information regarding real-time power consumption and generally lack intelligent decision-making capabilities for optimizing household energy usage. Moreover, consumers often face difficulties in monitoring individual appliance consumption, detecting energy wastage, and controlling electrical loads remotely. Recent advancements in Artificial Intelligence (AI), Internet of Things (IoT), cloud computing, and embedded systems have enabled the development of smart energy management platforms capable of continuously monitoring, analysing, and controlling household electrical loads. This project presents an AI-Based Home Energy Management System with Dynamic Cloud Using NodeMCU and PZEM. The proposed framework integrates a NodeMCU (ESP8266) controller, PZEM-004T energy meter, relay modules, AI-based energy optimization algorithms, and a cloud platform to provide intelligent monitoring and automatic control of household appliances. The PZEM sensor continuously measures electrical parameters including voltage, current, power, energy consumption, frequency, and power factor. The NodeMCU processes the acquired data and uploads them to a dynamic cloud platform through Wi-Fi for real-time monitoring and historical analysis. Artificial Intelligence analyses consumption patterns, predicts energy demand, identifies abnormal power usage, and automatically controls selected electrical loads to improve overall energy efficiency. Users can remotely monitor and control appliances through mobile or web dashboards while receiving notifications regarding excessive energy consumption. Experimental evaluation demonstrates accurate energy measurement, reliable cloud communication, low response time, and efficient AI-based load management. The proposed intelligent framework significantly reduces electricity consumption, improves household energy efficiency, supports predictive energy management, lowers electricity costs, and contributes to the development of smart homes and sustainable energy management systems. Keywords: Artificial Intelligence, Home Energy Management System, NodeMCU, ESP8266, PZEM-004T, Internet of Things (IoT), Smart Home, Cloud Computing, Energy Monitoring, Load Control, Energy Efficiency