Fine-tuning forecasting with AI
Farseer and AMART are part of a larger concerted effort by AES to expand its investments in renewable energy. For example, the company bought Valcour Wind Energy’s six wind farms in New York and also builds and operates solar farms and storage systems in California, Arizona, and several other US states, as well as in Brazil and Argentina. Those investments come just as the company claims, in its 2023 annual report, that demand from corporate data centers in the US is expected to roughly double within the next three years as generative AI deployments expand.
But with the addition of more renewable energy to its portfolio, weather uncertainty becomes a greater challenge for AES. The Farseer machine learning model represents a major advancement for the company because it analyzes large amounts of historical weather data to predict wind farm output with far greater accuracy than in the past, Reyes says.
“As we iterated with Farseer, we’ve been able to make the model more accurate because the model is looking at the history and updates every day,” Reyes points out. “It proposes next-day generation and then we get the actuals, and the actuals go into the history and continues to further refine what the model is giving you the next day.”
