Renewable Energy-Aware Carbon Digital Twin for Smart and Sustainable Construction Operations Using AI-Based Multi-Objective Optimization

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Smart and sustainable construction requires methods that jointly manage construction processes, carbon emissions, energy supply, and decision-making. This study develops a renewable energy (RE)-aware carbon digital twin using Siemens Tecnomatix Plant Simulation, version 2022 (Siemens Digital Industries Software, Plano, TX, USA), hereafter referred to as STPS, and artificial intelligence (AI)-based multi-objective optimization. The framework integrates activity records, equipment-demand profiles, renewable-generation data, battery-storage states, emission factors, cost parameters, schedule indicators, and reinforcement learning (RL) state–action–reward records within a discrete-event simulation environment. Construction activities are represented through event-driven source, queue, buffer, processor, resource-pool, transporter, event-controller, table-file, and sink objects, enabling predecessor validation, resource allocation, processing, completion tracking, and key performance indicator (KPI) updates. The energy layer coordinates equipment demand, solar photovoltaic (PV) generation, battery charging and discharging, grid electricity, diesel backup, RE share, battery state of charge (SOC), and emissions. The AI-control layer observes carbon, cost, delay, queue length, utilization, idle time, renewable share, and SOC; filters infeasible actions; evaluates corrective interventions; and ranks policies under carbon, delay, and SOC constraints. Three scenarios are evaluated: diesel–grid baseline operation, rule-based solar-battery operation, and AI-controlled renewable-aware operation. The AI-controlled scenario achieved 16,850 kg carbon dioxide equivalent (kg CO2e), a cost of 2.28 million United States dollars (M USD), a delay of 4.2 h, an 81.6% RE share, 86.7% resource utilization, and 8.6% idle time. Relative to the diesel–grid baseline, it reduced emissions by 32.2%, cost by 18.0%, delay by 66.7%, and diesel-equivalent energy by 97.7%, while increasing RE share by 69.3 percentage points. Reward-weight sensitivity results show that the balanced policy preserves low-carbon performance across stakeholder priorities. The findings demonstrate that integrating digital twins, RE dispatch, and AI-based decision control can support data-driven, optimized low-carbon construction management.

Links and sources

Comments