SYNAPCITY-DT: AI-Driven Urban Energy Intelligence for Smart Cities: A Decision Oriented Digital Twin Framework for Uncertainty Aware Renewable Urban Energy Management

Abstract

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.