arXiv:2411.11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by the great success of pre-trained language models (LLMs) in natural language processing, this paper proposes a zero and few shot load forecasting approach using an advanced LLM framework denoted as the Chronos model. By utilizing its extensive pre-trained knowledge, the Chronos model enables accura
The proliferation of powerful pre-trained LLMs has led researchers to explore their applicability beyond natural language processing, leveraging their pattern recognition capabilities for diverse tasks like time-series forecasting.
This development could significantly enhance the accuracy and efficiency of critical infrastructure management by enabling robust load forecasting even in data-scarce environments.
The reliance on extensive historical data for effective deep learning load forecasting is reduced, opening new possibilities for rapid deployment and adaptation in energy management systems.
- · Energy utilities
- · Smart grid developers
- · AI model developers
- · Regions with limited historical energy data
- · Traditional load forecasting model providers
- · Organizations heavily invested in purely data-intensive forecasting methods
More accurate energy demand predictions lead to optimized grid operations and reduced energy waste.
Improved energy efficiency and stability can support the integration of more intermittent renewable energy sources.
Enhanced load balancing capabilities mitigate risks of localized blackouts and reduce overall energy costs for consumers and industries.
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Read at arXiv cs.LG