The AI-Powered Solar Energy Management System (AI-SEMS) combines artificial intelligence with Battery Management Systems (BMS), inverter, and IoT technologies such as ThingSpeak and DHT11 sensors to create a comprehensive solution for optimizing solar energy utilization. Leveraging machine learning algorithm, AI-SEMS analyzes real-time data from solar panels, battery storage, and inverter performance to predict energy generation and consumption patterns. The BMS component ensures optimal battery health and performance by managing charging and discharging cycles based on predictive analytics. Inverters efficiently convert solar energy into usable power, while ThingSpeak facilitates remote monitoring and data visualization, enabling users to access critical system metrics in real time. Solar energy generated by the solar panel is stored in a rechargeable battery through a battery charging circuit. The stored battery power is then used to operate AC loads through an inverter. The main controller of the system is the Raspberry Pi Zero 2W, which features built-in Wi-Fi connectivity. The Raspberry Pi continuously monitors the battery voltage through a voltage sensor and automatically controls the battery charging process by switching a relay ON or OFF based on the battery voltage level. The relay acts as an electronic switch to connect or disconnect the charging circuit. The system also monitors various parameters such as battery voltage and current, solar panel voltage and current, temperature, humidity, light intensity, and solar panel efficiency. Since the Raspberry Pi does not have built-in analog input channels, an Arduino Uno is used as an Analog-to-Digital Converter (ADC) interface to acquire sensor data and transmit it to the Raspberry Pi. The Raspberry Pi displays the measured parameters on an LCD screen and uploads the real-time data to the ThingSpeak cloud platform through its built-in Wi-Fi module for remote monitoring and analysis.     Objectives:

1. To develop an AI-powered solar energy management system for efficient utilization of solar power.
2. To monitor solar panel voltage, current, battery voltage, and battery current in real time.
3. To predict energy generation and consumption patterns using machine learning techniques.
4. To optimize battery charging and discharging cycles through a Battery Management System (BMS).
5. To automatically control battery charging using a relay based on battery voltage levels.
6. To monitor environmental parameters such as temperature, humidity, and light intensity using sensors.
7. To calculate and analyze the efficiency of the solar panel system.
8. To display real-time system parameters on an LCD display.
9. To upload and monitor system data remotely through the ThingSpeak IoT platform using Wi-Fi.
10. To improve energy efficiency, battery life, and overall system reliability through intelligent control and monitoring.

        The major building blocks of this project are:

• Power supply.
• Raspberry pi ZERO 2W.
• SOLAR Panel.
• Charging circuit.
• Temperature sensor.
• Voltage sensor.
• Current sensor.
• Light Intensity Sensor/
• DHT11(temperature & humidity) sensor.
• 12v Battery pack.
• Relay.
• LCD display.
• LED Indicators

    Software’s used in the project:

1. Raspbian OS.
2. Python language.
3. Express SCH for Circuit design.
4. Machine learning (ML).

                    video: