EF-LLM: A Continual Learning-Based Framework for AI-Assisted Automation, Enhanced Sparse Prediction, and Hallucination Detection

Abstract

Accurate prediction helps achieve supply-demand balance in energy systems,thereby supporting operational decision-making and scheduling.Traditional models,which lack AI-assisted automation,often rely heavily on expert knowledge,incur high labor costs,and perform poorly under sparse-data conditions.To address these challenges,this work proposes an Energy Forecasting Large Language Model (EF-LLM),which integrates domain knowledge and temporal data for time-series forecasting while supporting both pre-forecast operations and post-forecast decision-making.EF-LLM's human-artificial intelligence (AI) interaction capabilities further lower the entry barrier for forecasting tasks and reduce the need for additional expert involvement.To support these functions, a continual learning framework with updatable Low-Rank Adaptation (LoRA) and a multi-channel architecture is developed to align heterogeneous multimodal data, enabling EF-LLM to continuously acquire and adapt multimodal knowledge.In addition,EF-LLM improves prediction accuracy under sparse-data conditions by leveraging its ability to process and fuse multimodal information.A Fusion Parameter-Efficient Fine-Tuning (F-PEFT) method is further introduced to effectively exploit both time-series data and textual information.EF-LLM is also the first energy-specific LLM to detect hallucinations and quantify their occurrence rate, achieved via multi-task learning and semantic similarity analysis.This work has achieved success in energy prediction scenarios for load, PV, and wind power forecast.