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As AES continues to expand its investments in renewable energy, the company is facing challenges posed by weather uncertainty. To address this issue, AES has developed a machine learning model called Farseer that analyzes historical weather data and predicts wind farm output with greater accuracy than before. The model has been refined over time through continuous iteration, allowing it to become more accurate as it incorporates real-world data updates.
According to Reyes, a representative from AES, the iterative process of refining Farseer has allowed the company to improve its forecasting capabilities and adapt to changing conditions. By analyzing historical data and incorporating real-world updates, the model can propose next-day generation predictions that are continually refined based on actual outcomes. This allows AES to fine-tune its forecasting capabilities and ultimately improve its efficiency and sustainability in the renewable energy sector.
In addition to developing Farseer, AES has also been acquiring wind farms in New York and developing solar farms and storage systems in various states in the US, as well as in Brazil and Argentina. The company’s 2023 annual report indicates that demand from corporate data centers in the US is expected to double in the next three years. Despite these challenges, AES remains committed to expanding its renewable energy portfolio and improving its efficiency and sustainability through innovative technologies like Farseer.
