Buildings consume nearly 40% of global energy and produce 36% of CO₂ emissions, making net-zero energy buildings (NZEBs) critical for climate mitigation. However, empirical validation of NZEB performance at commercial scale remains limited. This study presents a data-driven computational analysis of the Bullitt Center—a six-story, 50,071 sf office building in Seattle—to quantify its operational energy balance and ecosystem service value using AI-enhanced monitoring analytics.

Over 12 months, machine learning algorithms analyzed 8,760 hourly data points from IoT sensors monitoring energy consumption, solar generation, water usage, and occupant behavior. Statistical modeling and regression analysis identified performance drivers, while predictive algorithms optimized HVAC operations and demand response. Multi-criteria ecosystem services valuation employed computational modeling to quantify economic benefits over the building's 250-year design life.

The Bullitt Center achieved an Energy Use Intensity (EUI) of 9.4 kBTU/sf/year, performing 77% better than Seattle's baseline and producing 114,085 kWh annual energy surplus. AI-powered analytics revealed daylighting strategies reduced artificial lighting by 82%, while predictive HVAC controls maintained thermal comfort with minimal energy use. Smart rainwater harvesting and composting systems eliminated municipal water and wastewater connections. Ecosystem services valuation estimated USD 18.45 million in total benefits: energy efficiency (45%), carbon reduction (38%), and water conservation (12%). Benchmarking against 350 regional buildings confirmed superior performance.

This study demonstrates that integrated design processes, rigorous commissioning, and AI-driven monitoring enable genuine net-zero performance at commercial scale. The computational framework provides evidence-based guidelines for architects, engineers, and policymakers advancing climate-resilient intelligent building systems.