# EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting

arXiv: https://arxiv.org/abs/2505.01959 · HTML: https://mlsanity.com/paper/2505.01959/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters](https://mlsanity.com/paper/2201.10036/) (2022)
- [CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency](https://mlsanity.com/paper/2302.08681/) (2023)
- [Towards Sustainable Large Language Model Serving](https://mlsanity.com/paper/2501.01990/) (2024)
- [Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View](https://mlsanity.com/paper/2502.11256/) (2025)
- [GreenLLM: Disaggregating Large Language Model Serving on Heterogeneous GPUs for Lower Carbon Emissions](https://mlsanity.com/paper/2412.20322/) (2024)
- [CASPER: Carbon-Aware Scheduling and Provisioning for Distributed Web Services](https://mlsanity.com/paper/2403.14792/) (2024)
- [Uncertainty-Aware Decarbonization for Datacenters](https://mlsanity.com/paper/2407.02390/) (2024)

## Built on by (most cited first, 5 of 5)

- [CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Models](https://mlsanity.com/paper/2510.01521/) (2025)
- [Cache Your Prompt When It's Green: Carbon-Aware Caching for Large Language Model Serving](https://mlsanity.com/paper/2505.23970/) (2025)
- [Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors](https://mlsanity.com/paper/2509.26511/) (2025)
- [AgentDecarbonizer: Carbon-Aware Execution for AI Agents](https://mlsanity.com/paper/2608.20566/) (2026)
- [Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions](https://mlsanity.com/paper/2609.35569/) (2026)

## Abstract (arXiv)

Carbon intensity (CI) measures the average carbon emissions generated per unit of electricity, making it a crucial metric for quantifying and managing the environmental impact. Accurate CI predictions are vital for minimizing carbon footprints, yet the state-of-the-art method (CarbonCast) falls short due to its inability to address regional variability and lack of adaptability. To address these limitations, we introduce EnsembleCI, an adaptive, end-to-end ensemble learning-based approach for CI forecasting. EnsembleCI combines weighted predictions from multiple sublearners, offering enhanced flexibility and regional adaptability. In evaluations across 11 regional grids, EnsembleCI consistently surpasses CarbonCast, achieving the lowest mean absolute percentage error (MAPE) in almost all grids and improving prediction accuracy by an average of 19.58%. While performance still varies across grids due to inherent regional diversity, EnsembleCI reduces variability and exhibits greater robustness in long-term forecasting compared to CarbonCast and identifies region-specific key features, underscoring its interpretability and practical relevance. These findings position EnsembleCI as a more accurate and reliable solution for CI forecasting. EnsembleCI source code and data used in this paper are available at https://github.com/emmayly/EnsembleCI.