SYNAPCITY-DT: AI-Driven Urban Energy Intelligence for Smart Cities: A Decision Oriented Digital Twin Framework for Uncertainty Aware Renewable Urban Energy Management
Lopullinen julkaistu versio - 1.38 MB
Shikdar, T. A., & Laaksonen, H. (2026). SYNAPCITY-DT: AI-Driven Urban Energy Intelligence for Smart Cities: A Decision Oriented Digital Twin Framework for Uncertainty Aware Renewable Urban Energy Management. Street Art & Urban Creativity, 12(5), 285–321. https://doi.org/10.62161/sauc.v12.6349
Copyright (c) 2026 Authors retain copyright and transfer to the journal the right of first publication and publishing rights. This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Pysyvä osoite
Kuvaus
This study presents SYNAPCITY-DT, an AI-driven urban energy digital twin framework for adaptive renewable energy operation under uncertainty. The framework integrates probabilistic photovoltaic forecasting, risk-aware battery energy storage optimization, and operational intelligence within a unified digital twin environment. Using real operational data from a utility-scale PV–BESS system in Finland, the framework evaluates uncertainty propagation across forecasting, planning, and operational decision-making. Results show that adaptive risk-aware control improves operational resilience, flexibility utilization, and decision robustness under varying forecast horizons. The study demonstrates the potential of AI-enabled digital twins as scalable urban energy intelligence systems supporting resilient and sustainable smart cities.
Emojulkaisu
ISBN
ISSN
2183-9956
2183-3869
2183-3869
Aihealue
Kausijulkaisu
Street art & urban creativity|12
OKM-julkaisutyyppi
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä (vertaisarvioitu)