Machine learning can strengthen the business case for wind energy and drive further carbon-free energy on electric grids across the globe.

FREMONT, CA: Carbon-free technologies like renewable energy can combat climate change, but many have not reached their full ability. Over the years, wind farms have become an essential source of carbon-free electricity as the cost of turbines has plummeted, and adoption has increased. However, the variable nature of wind energy itself makes it an unpredictable energy source. Companies have started applying machine learning algorithms to wind power capacity. These wind farms collectively create as much electricity as is required by a medium-sized city.

Top 10 Wind Energy Solution Companies - 2020They leverage a neural network trained on extensively available weather forecasts and historical turbine data to predict wind power output 36 hours ahead of actual creation. Based on these predictions, the model recommends making optimal hourly delivery commitments to the energy grid a full day in advance. This is essential because energy sources that can be scheduled are often valuable to the grid. Although companies continue to refine their algorithm, machine learning across wind farms has created positive results. To date, machine learning has accelerated the value of wind energy by roughly 20 percent, compared to the baseline scenario of no time-powered commitments to the grid.

Firms cannot eliminate wind variability, but the early results suggest that firms can use machine learning to make wind power more predictable and valuable. This method also helps bring greater data rigor to wind farm operations. Machine learning can help wind farm operators make smarter, faster, and more data-driven evaluations of how their power output can meet electricity needs. Researchers and practitioners across the energy sector are developing new ideas for how society can make the most variable power sources like solar and wind. The energy companies are eager to join them in exploring these cloud-based machine learning strategies' general availability.

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