mlsanityData to 7 Oct 2026

Cloud-Edge Collaborative Large Models for Robust Photovoltaic Power Forecasting

arXiv 2603.22343 ↗First posted 21 Mar 2026cs.LG cs.NIcited by 0

Not on the map. Why?

Abstract (arXiv)

Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constraints. Existing models work well under normal conditions but often struggle with rare ramp events and unexpected weather changes. Relying solely on cloud-based large models often leads to significant communication delays, which can hinder timely and efficient forecasting in practical grid environments. To address these issues, we propose a condition-adaptive cloud-edge collaborative framework *CAPE* for PV forecasting. *CAPE* consists of three main modules: a site-specific expert model for routine predictions, a lightweight edge-side model for enhanced local inference, and a cloud-based large retrieval model that provides relevant historical cases when needed. These modules are coordinated by a screening module that evaluates uncertainty, out-of-distribution risk, weather mutations, and model disagreement. Furthermore, we employ a Lyapunov-guided routing strategy to dynamically determine when to escalate inference to more powerful models under long-term system constraints. The final forecast is produced through adaptive fusion of the selected model outputs. Experiments on two real-world PV datasets demonstrate that *CAPE* achieves superior performance in terms of forecasting accuracy, robustness, routing quality, and system efficiency.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 21 Mar 2026.

Builds on

19 of 36 references.

References · 19

  1. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  2. 2024Chronos: Learning the Language of Time Seriescited by 712
  3. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  4. 2023A decoder-only foundation model for time-series forecastingcited by 650
  5. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  6. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  7. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  8. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 150
  9. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 89
  10. 2024NetLLM: Adapting Large Language Models for Networkingcited by 76
  11. 2025LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligencecited by 65
  12. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 30
7 more
  1. 2024CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaborationcited by 25
  2. 2022A Survey on Collaborative DNN Inference for Edge Intelligencecited by 23
  3. 2025FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Datacited by 5
  4. 2025PV-VLM: A Multimodal Vision-Language Approach Incorporating Sky Images for Intra-Hour Photovoltaic Power Forecastingcited by 4
  5. 2023PoPeC: PAoI-Centric Task Offloading with Priority over Unreliable Channelscited by 3
  6. 2025A Novel Distributed PV Power Forecasting Approach Based on Time-LLMcited by 3
  7. 2026FORLER: Federated Offline Reinforcement Learning with Q-Ensemble and Actor Rectificationcited by 2

Built on by

Not cited yet.