Scholarlink

This project explores how machine learning can help optimize hybrid renewable energy systems, with a special focus on managing net metering costs. By analyzing real-time data from renewable sources and consumer energy usage, the goal is to create a smart, efficient framework that improves energy reliability while keeping costs low. The idea is to strike a balance ensuring that energy production and consumption align seamlessly with changing demand and environmental conditions. To make this happen, we’re using the Open Energy Modelling Framework (OEMOF), which helps optimize how energy is distributed, stored, and interacted with the grid. With OEMOF, we can simulate energy flows, make better decisions about energy trading and self-consumption, and develop cost-effective net metering strategies. On top of that, advanced predictive models for weather and energy demand forecasting allow for proactive system adjustments, making sure the setup remains efficient and reliable.Beyond the technical side, this approach directly supports the global shift toward sustainable energy. By making hybrid renewable energy systems more cost-effective and scalable, it not only helps individuals and businesses save money but also contributes to reducing carbon footprints moving us one step closer to a greener future.

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Data-Driven Optimization of Hybrid Renewable Energy Systems: Managing Net Metering Costs Through Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • V. K. Abhang,
  • Y. A. Shinde,
  • S. N. Shingote,
  • Somal Adik,
  • Nikhil Aglawe,
  • Vivek Akolkar,
  • Pratik Ghondage

摘要

This project explores how machine learning can help optimize hybrid renewable energy systems, with a special focus on managing net metering costs. By analyzing real-time data from renewable sources and consumer energy usage, the goal is to create a smart, efficient framework that improves energy reliability while keeping costs low. The idea is to strike a balance ensuring that energy production and consumption align seamlessly with changing demand and environmental conditions. To make this happen, we’re using the Open Energy Modelling Framework (OEMOF), which helps optimize how energy is distributed, stored, and interacted with the grid. With OEMOF, we can simulate energy flows, make better decisions about energy trading and self-consumption, and develop cost-effective net metering strategies. On top of that, advanced predictive models for weather and energy demand forecasting allow for proactive system adjustments, making sure the setup remains efficient and reliable.Beyond the technical side, this approach directly supports the global shift toward sustainable energy. By making hybrid renewable energy systems more cost-effective and scalable, it not only helps individuals and businesses save money but also contributes to reducing carbon footprints moving us one step closer to a greener future.