Forecast errors cost renewable energy operators and traders millions of dollars each year. Our AI method improves forecast accuracy by 13% for solar and up to 50% for wind, directly reducing imbalance costs and financial risk. For a typical 100 MW solar park, 13% higher accuracy translates into around $90K per year in avoided penalties. In highly volatile markets like ERCOT, a 50% reduction in wind forecast errors delivers multi-million-dollar savings annually. Our approach combines physics-based weather models with AI trained on real output data, capturing the true behavior of solar panels and wind turbines, from shading and snow cover to wake effects and turbine aging.. ✔️Reduce financial risk by narrowing uncertainty bands ✔️Anticipate grid fluctuations with greater confidence ✔️Optimize asset performance and revenue stability Ready to see how AI-enhanced forecasting could improve your operations or trading strategy? Read our insights and then reach out to our experts to have a closer look at the potential ROI for your portfolio: https://lnkd.in/ea9-3Znm
