EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection
Abstract: Accurate prediction helps to achieve supply-demand balance in energy systems, supporting decision-making and scheduling. Traditional models, lacking AI-assisted automation, rely on experts, incur high costs, and struggle with sparse data prediction. To address these challenges, we propose the Energy Forecasting LLM (EF-LLM), which integrates domain knowledge and temporal data for time-series forecasting, supporting both pre-forecast operations and post-forecast decision-support. EF-LLM's human-AI interaction capabilities lower the entry barrier in forecasting tasks, reducing the need for extra expert involvement. To achieve this, we propose a continual learning approach with updatable LoRA and a multi-channel architecture for aligning heterogeneous multimodal data, enabling EF-LLM to continually learn heterogeneous multimodal knowledge. In addition, EF-LLM enables accurate predictions under sparse data conditions through its ability to process multimodal data. We propose Fusion Parameter-Efficient Fine-Tuning (F-PEFT) method to effectively leverage both time-series data and text for this purpose. EF-LLM is also the first energy-specific LLM to detect hallucinations and quantify their occurrence rate, achieved via multi-task learning, semantic similarity analysis, and ANOVA. We have achieved success in energy prediction scenarios for load, photovoltaic, and wind power forecast.
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FPE-LLM: A Simple Explanation
What is this paper about?
This paper introduces FPE-LLM, a smart AI system designed to predict important things in energy systems, like how much electricity people will use, how much solar power will be produced, or how prices might change. It doesn’t just give numbers—it can also talk to users, explain its predictions, and help with follow-up tasks, almost like a helpful expert.
What questions is the paper trying to answer?
The researchers focus on three big goals:
- How can we make predictions work well even when rare or extreme events happen (like sudden storms or unusual holidays), even with very little data?
- How can we reduce the need for human experts, so non-experts can still get good predictions and guidance?
- How can we reduce “hallucinations,” which are confident but wrong answers from AI?
How does FPE-LLM work?
Think of FPE-LLM as a team where different parts do different jobs and then work together.
- Time-series understanding: Time-series is data that changes over time (like electricity use every 15 minutes). FPE-LLM uses a method called “prefix tuning” to help it understand and use this kind of data.
- Text understanding: It also understands written information (like weather reports, rules, or notes). It uses “LoRA” to adjust how it talks and reasons in text, so it answers clearly in the desired format.
- Mixing different data types: Real energy problems include both numbers (time-series) and text (weather, rules). FPE-LLM uses a “multi-channel” system to handle both at the same time. It separates them with special placeholder tokens, like putting dividers in a binder, so the AI knows which part is numbers and which part is text.
- Function calling: For exact math (like precise accuracy scores or calculations from formulas), the AI can call an external “calculator function” to avoid small math mistakes. It’s like asking a calculator for the exact number.
- Multi-task learning: The model learns to do multiple related tasks at once (for example, predicting a value and also predicting which range that value falls into). This helps it learn more robustly and also helps detect when something seems off.
Here are some technical terms explained simply:
- Prefix tuning: Imagine giving the AI a small hint before the main input to guide how it should think about the numbers.
- LoRA (Low-Rank Adaptation): A lightweight way to fine-tune the AI’s language “style and knowledge” without changing everything inside.
- Multimodal: Using different types of inputs (numbers + text) together.
- Hallucination: When the AI sounds confident but gives an answer that’s not supported by the data.
What did the researchers test?
They tested FPE-LLM on three real energy problems:
- Load forecasting: Predicting how much electricity homes will use (data from Ausgrid in Australia).
- Solar (PV) forecasting: Predicting how much electricity a solar plant will produce (data from China Southern Power Grid).
- Electricity price forecasting: Predicting energy market prices (data from AEMO in Australia).
They compared FPE-LLM to common time-series models like LSTM, TimesNet, and iTransformer.
What did they find and why is it important?
Here are the main findings:
- Strong results on regular patterns: FPE-LLM did very well on load and solar predictions, especially when the data wasn’t too chaotic. It often beat or matched advanced time-series models.
- Great with rare events using text (few-shot): When unusual events happened (like “heavy rain turning to clear”), adding a short text description helped FPE-LLM make better predictions. This worked especially well with a Chain-of-Thought (CoT) approach—first predict with numbers, then add the special text hint and predict again. Accuracy improved after adding these few-shot descriptions.
- Helpful conversations and follow-up steps: FPE-LLM can explain results, help with tasks after the prediction (like checking whether results meet solar grid-connection rules), and guide users—even if they’re not experts. With function calling, it can get exact calculations 100% correctly for tasks that need precise math.
- Detecting hallucinations with fixed formats: When the model is trained to answer in a fixed, clear format (like a template), it’s easy to spot when it “goes off-script.” The researchers used this idea to detect when the model might be hallucinating. They also showed that changing how much the model focuses on each task can increase or decrease the chance of hallucinations.
- Stable predictions: Running the model 100 times gave very similar results (based on ANOVA statistics). That means its outputs are consistent and reliable.
A key limitation:
- Very irregular problems (like wild price swings) are harder. Even when FPE-LLM correctly predicts a range, turning that into a single number can still lead to big errors because the prices vary a lot (from negative to very high values).
Why does this matter?
- Easier forecasting for everyone: FPE-LLM lowers the barrier to doing energy forecasting. Non-experts can ask questions and get understandable answers and instructions.
- Better handling of rare events: By mixing text (like “storm passed quickly”) with numbers, the model can adapt better to extreme or unusual situations than traditional models.
- More reliable answers: Using fixed formats, multi-task learning, and function calls makes the results clearer, safer, and easier to check.
- Practical impact: With more data and knowledge added over time, FPE-LLM could take over many tasks that now require experienced engineers, helping energy systems run more efficiently and reliably.
Bottom line
FPE-LLM is a smart, flexible forecasting tool for the energy world. It blends number-crunching with language understanding, handles rare events with few examples, talks to users like an expert, and has built-in ways to spot and reduce mistakes. It works best on tasks with more regular patterns (like load and solar) and shows how AI can make complex energy planning faster, cheaper, and more dependable.
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