The expansion of solar energy has accelerated immensely in recent years. Today, solar supplies approximately 6.9 percent of global electricity, up from about 5.6 percent in 2023. Despite this rapid growth and enormous potential, many companies, organizations, and industries remain hesitant to fully embrace solar power because of its intermittent output and efficiency limitations.
At the same time, machine learning (ML) and Edge AI are revolutionizing efficiency across a range of industries by enabling smarter, data-driven decision-making. Beyond solar, ML enhances efficiency in manufacturing through predictive maintenance and process automation, reduces energy waste in smart grids via real-time load forecasting, and boosts agricultural productivity by powering precision farming techniques.
This white paper will provide an outline of an AI-driven solution for MPPT that greatly improves the system’s predictability and allows for proactive maintenance. In the white paper you will find:
- A detailed description of an AI-driven solution for the optimal duty cycle prediction for a DC-DC converter connected to a solar panel.
- How the optimized model achieves a 23× reduction in latency and a 42× decrease in energy consumption against comparative platforms.
- How the solution enables predictive maintenance by offering continuous insights into the system’s performance based on the AI model predictions.
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