Summary of Ef-llm: Energy Forecasting Llm with Ai-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection, by Zihang Qiu et al.
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection
by Zihang Qiu, Chaojie Li, Zhongyang Wang, Renyou Xie, Borui Zhang, Huadong Mo, Guo Chen, Zhaoyang Dong
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Energy Forecasting Large Language Model (EF-LLM) integrates domain knowledge and temporal data for time-series forecasting in energy systems. It leverages human-AI interaction capabilities to reduce the need for expert involvement and provides accurate predictions even with sparse data. The EF-LLM combines a multi-channel architecture for heterogeneous multimodal data alignment, continual learning through LoRA updates, and Fusion Parameter-Efficient Fine-Tuning (F-PEFT) to leverage both time-series data and text. This approach enables the model to detect hallucinations and quantify their occurrence rate via multi-task learning, semantic similarity analysis, and ANOVA. The authors demonstrate success in energy prediction scenarios for load, photovoltaic, and wind power forecast. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new AI-powered forecasting model called EF-LLM that helps balance supply and demand in the energy industry. Traditional models are not very good at this task because they rely too much on human experts and don’t handle missing data well. The EF-LLM is better because it can learn from lots of different sources, like weather forecasts and past energy usage patterns. This makes it more accurate and helps reduce costs. The model also has a special feature that lets it detect when its predictions are not based on real information, which is important for making good decisions. |
Keywords
* Artificial intelligence * Alignment * Continual learning * Fine tuning * Large language model * Lora * Multi task * Parameter efficient * Time series
