# Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting

arXiv: https://arxiv.org/abs/2409.00368 · HTML: https://mlsanity.com/paper/2409.00368/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room](https://mlsanity.com/paper/2602.08949/) (2026)
- [A Governance-Aware Large Language Model Orchestrated Agentic Digital Twin for Transmission System Operator Control Room Decision Support](https://mlsanity.com/paper/2609.22476/) (2026)

## Abstract (arXiv)

In this paper, we introduce a synergistic approach between artificial intelligence and system operators through an innovative digital twin architecture, integrated with an active learning framework, to enhance short-term load forecasting. Central to this architecture is the incorporation of sophisticated data pipelines, facilitating the real-time ingestion, processing and analysis of grid-related data. Utilizing a recurrent neural network architecture, our model generates day-ahead load forecasts together with prediction confidence intervals, strengthening system operator trust in the model's predictive reliability and enhancing their ability to respond to evolving grid conditions effectively. The active learning framework iteratively refines the predictions by incorporating real-time feedback based on forecast uncertainty, utilizing newly available data to continuously enhance forecasting accuracy and confidence. This AI-assisted strategy is exemplified in a case study of the Greek transmission system. It demonstrates the potential to transform short-term load forecasting, thereby increasing the reliability and operational efficiency of modern power grids. This approach marks a significant step forward in the digitalization and intelligent management of power systems.