EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting

TL;DR

EnsembleCI is an adaptive ensemble learning approach that significantly improves the accuracy and robustness of carbon intensity forecasting across regional grids, outperforming the existing state-of-the-art method.

Contribution

This paper introduces EnsembleCI, a novel ensemble learning framework that enhances regional adaptability and prediction accuracy for carbon intensity forecasting.

Findings

EnsembleCI achieves an average 19.58% improvement in prediction accuracy over CarbonCast.

EnsembleCI reduces variability and increases robustness in long-term forecasts.

EnsembleCI identifies region-specific key features, improving interpretability.

Abstract

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.