LiteCast: A Lightweight Forecaster for Carbon Optimizations

2025-11-09

AI Summary

Existing grid carbon intensity forecasting models incur high computational overhead, rely heavily on long-term historical data, and often yield diminishing energy-saving returns despite improved accuracy. This paper proposes LiteCast, a lightweight forecasting framework that abandons the pursuit of absolute prediction accuracy in favor of preserving rank-order consistency between predicted and actual carbon intensities—a property sufficient to enable near-optimal carbon-aware scheduling. LiteCast requires only several days of energy consumption and meteorological data, employs a low-complexity temporal modeling architecture, and supports minute-scale training and frequent dynamic updates. Evaluated across 50 real-world load traces from diverse global regions, LiteCast achieves a 20% improvement in energy savings over state-of-the-art methods, reaching 97% of the theoretical maximum savings. The framework significantly enhances scalability and practical deployability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Over recent decades, electricity demand has experienced sustained growth through widespread electrification of transportation and the accelerated expansion of Artificial Intelligence (AI). Grids have managed the resulting surges by scaling generation capacity, incorporating additional resources such as solar and wind, and implementing demand-response mechanisms. Altogether, these policies influence a region's carbon intensity by affecting its energy mix. To mitigate the environmental impacts of consumption, carbon-aware optimizations often rely on long-horizon, high-accuracy forecasts of the grid's carbon intensity that typically use compute intensive models with extensive historical energy mix data. In addition to limiting scalability, accuracy improvements do not necessarily translate into proportional increases in savings. Highlighting the need for more efficient forecasting strategies, we argue that carbon forecasting solutions can achieve the majority of savings without requiring highly precise and complex predictions. Instead, it is the preservation of the ranking of forecasts relative to the ground-truth that drives realized savings. In this paper, we present LiteCast, a lightweight time series forecasting method capable of quickly modeling a region's energy mix to estimate its carbon intensity. LiteCast requires only a few days of historical energy and weather data, delivering fast forecasts that can quickly adapt to sudden changes in the electrical grid. Our evaluation in 50 worldwide regions under various real-world workloads shows that LiteCast outperforms state-of-the-art forecasters, delivering 20% higher savings with near-optimal performance, achieving 97% of the maximum attainable average savings, while remaining lightweight, efficient to run, and adaptive to new data.

Problem

• Developing lightweight carbon intensity forecasting for electricity grid optimization
• Reducing computational complexity while maintaining forecast ranking accuracy
• Enabling rapid adaptation to sudden grid changes with minimal historical data

Innovation

• Lightweight time series forecasting for carbon intensity
• Uses minimal historical energy and weather data
• Adapts quickly to sudden grid changes efficiently