Forecasting Marginal Emissions Factors in PJM (Proposals Track)

Amy H Wang (Western University); Priya L Donti (Carnegie Mellon University)

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Many climate change applications rely on accurate forecasts of power grid emissions, but many forecasting methods can be expensive, sensitive to input errors, or lacking in domain knowledge. Motivated by initial experiments using deep learning and power system modeling techniques, we propose a method that combines the strengths of both of these approaches to forecast hourly day-ahead MEFs for the PJM region of the United States.

Recorded Talk (direct link)