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

TL;DR

This paper presents an innovative digital twin architecture integrated with active learning and AI to improve day-ahead load forecasting, enhancing grid reliability and operator decision-making.

Contribution

It introduces a novel digital twin framework with active learning for real-time, accurate load forecasting, combining AI and system operator insights.

Findings

Improved forecast accuracy through active learning feedback.

Enhanced operator trust via prediction confidence intervals.

Case study demonstrates increased grid reliability.

Abstract

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.