RIS-LLM: A New Method for Electricity Price Prediction Using Large Language Models Integrating Reasoning Ability and Semantic Understanding

RIS-LLM (Reasoning-Informed Semantic Large Language Model) is an innovative open-source project that aims to combine the semantic understanding and reasoning capabilities of large language models, specifically for electricity price prediction tasks. This project addresses the limitations of traditional electricity price prediction methods (such as statistical models and conventional AI models) in handling multi-source heterogeneous data (structured numerical data + unstructured text), exploring innovative applications of AI in the energy sector.

As an infrastructure of the modern economy, electricity market price fluctuations directly affect industrial production and residents' lives. Traditional electricity price prediction methods rely on statistical models like ARIMA and GARCH, as well as time series analysis. However, electricity prices are dynamically influenced by multiple complex factors such as weather, supply and demand, policies, fuel prices, and the intermittency of renewable energy, making prediction extremely challenging. In recent years, machine learning/deep learning models have been applied to energy prediction, but traditional neural networks lack deep understanding of semantic information and struggle to integrate key information from unstructured text data like news, policies, and weather reports.

The core idea of RIS-LLM is to use the text understanding and reasoning capabilities of large language models to extract valuable information from multi-source heterogeneous data and convert it into predictive features, while handling both structured data (historical electricity prices, load data) and unstructured text data (policy announcements, market news, weather forecasts). Its technical architecture includes four key components:

1. Semantic Encoding Module: Pre-trained large language models deeply encode text data, extract semantic features, and capture implicit patterns such as policy changes, market dynamics, and weather warnings;
2. Reasoning Enhancement Layer: Simulate the thinking process of human experts through Chain-of-Thought to analyze causal relationships affecting electricity prices (e.g., extreme high temperatures → increased air conditioning load → rising demand → higher electricity prices);
3. Multimodal Fusion Mechanism: Fuse text semantic features with numerical time series features to build a unified representation space;
4. Temporal Modeling Component: Use Transformer or LSTM to handle the time series characteristics of electricity prices, capturing long-term trends and short-term fluctuations.

The main innovation of RIS-LLM lies in introducing the cognitive ability of 'reasoning' into electricity price prediction, which distinguishes it from traditional pure numerical regression models and allows the model to 'understand' the factors affecting electricity prices and their interactions. The advantages include:

• Enhanced Interpretability: The explicit reasoning process makes prediction results traceable and explainable;
• Improved Data Efficiency: Using pre-trained knowledge, it can achieve good results even in small-sample scenarios;
• Stronger Adaptability: Quickly adapts to external shocks such as policy changes and market structure adjustments;
• Multi-task Capability: Can be extended to related energy tasks such as load prediction and renewable energy output prediction.

Accurate electricity price prediction has important economic value for electricity market participants: power generation companies optimize their generation plans and bidding strategies; electricity retailers manage purchase costs and pricing strategies; large industrial users choose optimal electricity consumption periods to reduce electricity bills; grid dispatch departments perform precise balance dispatch. The open-source implementation of RIS-LLM provides researchers and practitioners with a reproducible and scalable technical framework, promoting the application of AI in the energy sector.

The technical implementation details of RIS-LLM include:

• Data Preprocessing: Electricity price data cleaning and normalization, text data tokenization and vectorization, time alignment of multi-source data;
• Model Training Strategy: May adopt incremental learning or online learning to adapt to dynamic market changes;
• Evaluation Metrics: In addition to traditional metrics like RMSE and MAPE, probabilistic prediction evaluation methods may be introduced;
• Inference Optimization: Use techniques such as model quantization and knowledge distillation to improve deployment efficiency in resource-constrained environments.

The future development directions of RIS-LLM include:

• Multi-market Expansion: Expand from a single electricity market to cross-regional and multi-type electricity market prediction;
• Real-time Prediction Capability: Combine with streaming computing frameworks to achieve minute-level/second-level real-time prediction;
• Deepening Causal Reasoning: Introduce advanced causal inference methods to distinguish between correlation and causality, improving prediction robustness;
• Decision Support Integration: Combine prediction results with optimization algorithms to output optimal decision recommendations instead of just predictive values.

RIS-LLM demonstrates the great potential of large language models in vertical domain applications. By integrating semantic understanding and reasoning capabilities, it provides a new solution for electricity price prediction. With the deepening of energy transition and electricity market reform, such intelligent prediction technologies will play an increasingly important role in ensuring energy security, improving market efficiency, and promoting the integration of renewable energy.