Neural Optimization Models for Ambient Energy Harvesting Efficiency Enhancement

The fast growth of Internet of Things (IoT) devices has made more people want electronic systems that are both environmentally friendly and self-powered. IoT devices that run on batteries need to be charged or replaced often, which makes maintenance more expensive and limits their ability to grow. Ambient energy harvesting has become an effective solution by gathering energy from renewable sources in the environment, like sunlight, light intensity, and the weather around it. This paper describes an ambient energy harvesting system that uses neural optimisation to make low-power IoT applications more reliable and efficient. The proposed system includes a solar panel, a DHT11 sensor, an LDR sensor, an ESP8266 NodeMCU microcontroller, a TP4056 battery management module, a lithium battery, an OLED display, and LED indicators. Environmental data such as temperature, humidity, and light intensity are continuously monitored and processed by the ESP8266 controller. Neural optimisation logic figures out how much energy is available and changes how energy is used in real time to get the most out of it. The TP4056 intelligent charging system safely stores the solar energy that has been collected, protecting the battery and providing a stable power supply. Experimental analysis shows better energy use, less waste, longer battery life, and the ability to monitor in real time. The proposed model offers a cost-effective, scalable, and sustainable solution for smart cities, remote sensors, and future green IoT infrastructures.