# Nowcasting day-ahead marginal emissions using multi-headed CNNs and deep generative models

arXiv: https://arxiv.org/abs/2310.01524 · HTML: https://mlsanity.com/paper/2310.01524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)

## Abstract (arXiv)

Nowcasting day-ahead marginal emissions factors is increasingly important for power systems with high flexibility and penetration of distributed energy resources. With a significant share of firm generation from natural gas and coal power plants, forecasting day-ahead emissions in the current energy system has been widely studied. In contrast, as we shift to an energy system characterized by flexible power markets, dispatchable sources, and competing low-cost generation such as large-scale battery or hydrogen storage, system operators will be able to choose from a mix of different generation as well as emission pathways. To fully develop the emissions implications of a given dispatch schedule, we need a near real-time workflow with two layers. The first layer is a market model that continuously solves a security-constrained economic dispatch model. The second layer determines the marginal emissions based on the output of the market model, which is the subject of this paper. We propose using multi-headed convolutional neural networks to generate day-ahead forecasts of marginal and average emissions for a given independent system operator.