Multi-task Online Learning for Probabilistic Load Forecasting

TL;DR

This paper introduces a multi-task online learning method for probabilistic load forecasting that effectively captures dynamic similarities among multiple entities, improving prediction accuracy and uncertainty assessment in power systems.

Contribution

It presents a novel multi-task learning approach for online probabilistic load forecasting that accounts for dynamic consumption patterns and uncertainties, outperforming existing methods.

Findings

Significantly improves load forecasting accuracy across diverse scenarios.

Effectively models inherent uncertainties in load demand.

Enhances multi-task learning performance for dynamic consumption patterns.

Abstract

Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions, buildings). Techniques based on multi-task learning obtain predictions by leveraging consumption patterns from the historical load demand of multiple entities and their relationships. However, existing techniques cannot effectively assess inherent uncertainties in load demand or account for dynamic changes in consumption patterns. This paper proposes a multi-task learning technique for online and probabilistic load forecasting. This technique provides accurate probabilistic predictions for the loads of multiple entities by leveraging their dynamic similarities. The method's performance is evaluated using datasets that register the load demand of multiple entities and contain diverse and dynamic consumption patterns. The experimental results show that the proposed method can significantly enhance the effectiveness of current multi-task learning approaches across a wide variety of load consumption scenarios.