Resilient Forecasting of High-Dimensional Network Time Series in the Energy Domain: A Hybrid Approach

Milena Petkovic () and Janina Zittel ()
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Milena Petkovic: Zuse Institute Berlin
Janina Zittel: Zuse Institute Berlin

Chapter Chapter 48 in Operations Research Proceedings 2023, 2025, pp 375-381 from Springer

Abstract: Abstract Energy systems are complex networks consisting of various interconnected components. Accurate energy demand and supply forecasts are crucial for efficient system operation and decision-making. However, high-dimensional data, complex network structures, and dynamic changes and disruptions in energy networks pose significant challenges for forecasting models. To address this, we propose a hybrid approach for resilient forecasting of network time series (H-NTS) in the energy domain. Our approach combines mathematical optimization methods with state-of-the-art machine learning techniques to achieve accurate and robust forecasts for high-dimensional energy network time series. We incorporate an optimization framework to account for uncertainties and disruptive changes in the energy system. The effectiveness of the proposed approach is demonstrated through a case study of forecasting energy demand and supply in a complex, large-scale natural gas transmission network. The results show that the hybrid approach outperforms alternative prediction models in terms of accuracy and resilience to structural changes and disruptions, providing stable, multi-step ahead forecasts for different short to mid-term forecasting horizons.

Keywords: Multivariate time series; Mathematical optimization; Energy networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-58405-3_48

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