Integrating Climate and Economic Predictors in Hybrid Prophet–(Q)LSTM Models for Sustainable National Energy Demand Forecasting: Evidence from The Netherlands

Ruben Curiël, Ali Mohammed Mansoor Alsahag () and Seyed Sahand Mohammadi Ziabari
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Ruben Curiël: Informatics Institute, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands
Ali Mohammed Mansoor Alsahag: Informatics Institute, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands
Seyed Sahand Mohammadi Ziabari: Informatics Institute, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands

Sustainability, 2025, vol. 17, issue 19, 1-48

Abstract: Forecasting national energy demand is challenging under climate variability and macroeconomic uncertainty. We assess whether hybrid Prophet–(Q)LSTM models that integrate climate and economic predictors improve long-horizon forecasts for The Netherlands. This study covers 2010–2024 and uses data from ENTSO-E (hourly load), KNMI and Copernicus/ERA5 (weather and climate indices), Statistics Netherlands (CBS), and the World Bank (macroeconomic and commodity series). We evaluate Prophet–LSTM and Prophet–QLSTM, each with and without stacking via XGBoost, under rolling-origin cross-validation; feature choice is guided by Bayesian optimisation. Stacking provides the largest and most consistent accuracy gains across horizons. The quantum-inspired variant performs on par with the classical ensemble while using a smaller recurrent core, indicating value as a complementary learner. Substantively, short-run variation is dominated by weather and calendar effects, whereas selected commodity and activity indicators stabilise longer-range baselines; combining both domains improves robustness to regime shifts. In sustainability terms, improved long-horizon accuracy supports renewable integration, resource adequacy, and lower curtailment by strengthening seasonal planning and demand-response scheduling. The pipeline demonstrates the feasibility of integrating quantum-inspired components into national planning workflows, using The Netherlands as a case study, while acknowledging simulator constraints and compute costs.

Keywords: energy demand forecasting; sustainability; energy transition; renewable integration; grid resilience; resource adequacy; hybrid classical–quantum models; QLSTM; Prophet; stacked generalisation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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