Developing a Multi-Region Stacking Ensemble Framework via Scenario-Based Digital Twin Simulation for Short-Term Household Energy Demand Forecasting
Source abstract
Abstract
Modern energy grids, with their regional diversity and complex consumption patterns, require accurate short-term forecasting for operational efficiency and reliability. This study introduces a Stacking Ensemble Forecasting (SEF) framework for multi-region household energy demand, utilizing an optimized stacking ensemble model tuned via Bayesian Optimization to achieve superior predictive accuracy. The framework significantly improved accuracy across Diyarbakır, Istanbul, and Odemis, with a final model demonstrating up to 16.47% RMSE reduction compared to the best baseline models. The final model’s real-world performance was validated through a Simulated Digital Twin (SDT) environment, where scenario-based testing demonstrated its robustness against behavioral changes, data quality issues, and device failures. The proposed SEF-SDT framework offers a generalizable solution for managing diverse regions and consumption profiles, contributing to efficient and sustainable energy management.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Energy Efficiency and Management · Energy, Environment, and Transportation Policies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Software Engineering, Manisa Celal Bayar University, 45140 Manisa, Turkey
Department of Electrical Engineering, Manisa Celal Bayar University, 45140 Manisa, Turkey
Department of Power Systems, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania
Funding
Ministry of Research, Innovation and Digitalization
References (38)
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