Cloud-Edge Collaborative Large Models for Robust Photovoltaic Power Forecasting
arXiv:2603.22343v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constraints. Local specialized models are efficient for routine conditions but often degrade under rare ramp events and unseen weather patterns, whereas always relying on cloud-side large models incurs substantial communication delay and cloud overhead. To address this challenge, we propose a risk-aware cloud-edge collaborative framework for latency-sensitive PV forecasting. The framework integrates a site-specific expert predictor for routine cases, a lightweight edge-side model for enhanced local inference, and a cloud-side large retrieval model that provides matched historical context when needed through a retrieval-prediction pipeline. A lightweight screening module estimates predictive uncertainty, out-of-distribution risk, weather mutation intensity, and model dis
arXiv:2603.22343v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constraints. Local specialized models are efficient for routine conditions but often degrade under rare ramp events and unseen weather patterns, whereas always relying on cloud-side large models incurs substantial communication delay and cloud overhead. To address this challenge, we propose a risk-aware cloud-edge collaborative framework for latency-sensitive PV forecasting. The framework integrates a site-specific expert predictor for routine cases, a lightweight edge-side model for enhanced local inference, and a cloud-side large retrieval model that provides matched historical context when needed through a retrieval-prediction pipeline. A lightweight screening module estimates predictive uncertainty, out-of-distribution risk, weather mutation intensity, and model disagreement, while a Lyapunov-guided router selectively escalates inference to the edge-small or cloud-assisted branches under long-term latency, communication, and cloud-usage constraints. The outputs of the activated branches are combined through adaptive fusion. Experiments on two real-world PV datasets demonstrate a favorable overall trade-off among forecasting accuracy, routing quality, robustness, and system efficiency.
Executive Summary
This article proposes a risk-aware cloud-edge collaborative framework for latency-sensitive photovoltaic power forecasting. The framework integrates a site-specific predictor, a lightweight edge-side model, and a cloud-side large retrieval model to enhance forecasting accuracy, robustness, and system efficiency. A screening module estimates predictive uncertainty and weather-driven distribution shifts, while a Lyapunov-guided router escalates inference to edge or cloud-side branches under latency constraints. Experiments on two real-world PV datasets demonstrate a favorable trade-off among forecasting accuracy, routing quality, robustness, and system efficiency. The framework addresses the challenge of balancing forecasting accuracy, robustness, and latency constraints in edge-enabled grids.
Key Points
- ▸ The framework integrates a site-specific predictor, a lightweight edge-side model, and a cloud-side large retrieval model.
- ▸ A screening module estimates predictive uncertainty and weather-driven distribution shifts.
- ▸ A Lyapunov-guided router escalates inference to edge or cloud-side branches under latency constraints.
Merits
Strength in Addressing the Trade-Off Among Forecasting Accuracy, Robustness, and Latency Constraints
The framework effectively balances the trade-off among forecasting accuracy, robustness, and latency constraints, making it suitable for latency-sensitive photovoltaic power forecasting in edge-enabled grids.
Demerits
Limitation in Generalizability to Different Weather Patterns
The framework's performance may degrade in the presence of rare or unseen weather patterns, which may limit its generalizability to different weather conditions.
Expert Commentary
The article presents a well-structured and comprehensive framework for latency-sensitive photovoltaic power forecasting. The integration of a site-specific predictor, a lightweight edge-side model, and a cloud-side large retrieval model is a significant contribution to the field. However, the framework's performance under rare or unseen weather patterns warrants further investigation. The article's implications for practical and policy applications are substantial, and the framework has the potential to be deployed in real-world photovoltaic power forecasting systems.
Recommendations
- ✓ Future research should focus on improving the framework's generalizability to different weather patterns and evaluating its performance in real-world deployments.
- ✓ The framework's architecture and components can be further developed and optimized for real-time processing and reduced latency.
Sources
Original: arXiv - cs.LG
