## 2 Problem Description--Energy Monitoring in Industries  
Efficient monitoring of electricity and air consumption in factories is essential for cost reduction and financial sustainability. By identifying inefficiencies and optimizing resource usage, manufacturers can lower utility costs while advancing toward net-zero emissions goals. Responsible resource management is increasingly critical as energy costs rise and environmental regulations tighten. Energy and consumption monitoring has been extensively studied in various research works. For example, [PERSON] et al. [10] developed an online energy monitoring and analysis tool to measure the energy consumption of individual components in a milling machine. Given the machine's stock configuration, they retrofitted it with a power monitoring PLC module and integrated it with the main PLC, enabling external data requests via the OPC-UA interface. By analyzing the captured energy data, they identified inefficiencies and optimized the machine's auxiliary components, achieving a 28% reduction in power consumption without altering the machining process, including the feed rate and spindle operation. In another study, [PERSON] et al. [11] retrofitted a 20-year-old CNC machine with an energy meter, enabling wireless data transmission via the Energy Window ZigBee protocol (IEEE 802.15 [12]) to a Power Link concentrator (Schneider Electric). The data were then sent via Modbus over TCP/IP to a Siemens IoT 2040 gateway, which aggregated and transmitted it to Siemens MindSphere (Version 3.0) and GRV Software Cloud (Version 0.3.2), integrating the machine into an IoT-enabled smart manufacturing system. In another study, [PERSON] et al. [13] developed an IoT-driven energy monitoring system that utilized edge computing to optimize industrial energy management. Their system was structured into three main layers: an energy metering system, an edge gateway, and a cloud layer. At the device level, wired and wireless energy meters were used to monitor both movable and stationary assets, collecting key consumption parameters and transmitting data in JSON format to an edge server. The edge gateway processed these data, stored them in a PostgreSQL database, computed additional parameters, and provided a local interface for real-time monitoring. Additionally, filtered data were sent to the cloud for remote access. The system was designed for efficient data handling, minimizing CPU usage (5.58%) while mitigating network-related disruptions, thereby enhancing reliability in industrial energy monitoring. [PERSON] et al. [14] developed an IoT-enabled smart energy meter that utilized a ESP32 microcontroller, a Hall effect sensor, a voltage sensor, and ThingSpeak software (Version 2.0.1) to monitor energy consumption in real time. Their system incorporated key components, including an SCT103 Hall effect sensor for current measurement and a ZMPT101B sensor for voltage detection, to ensure precise data collection. The ESP32 microcontroller functioned as the central processing unit, facilitating data transmission via Wi-Fi to the ThingSpeak platform, where users could track energy consumption trends and patterns. A 16 \(\times\) 2 LCD display, connected through the I2C communication protocol, presented real-time power usage information. Additionally, the system supported advanced features such as graphical data visualization, continuous monitoring, and potential future improvements, including automated alerts for excessive power consumption. Their findings demonstrated the potential of IoT in energy management, enabling users to make informed decisions for optimizing energy use and promoting sustainability. [PERSON] et al. [15] developed a cost-effective electricity monitoring system using the WeMos IoT module and ACS712-30 sensor, designed for seamless home automation integration. Utilizing wireless technology, the system ensured easy installation, scalability, and mobile compatibility.