EXPLORING SUSTAINABILITY PARAMETERS FOR NET-ZERO BUILDING IMPLEMENTATION USING AI-DRIVEN GENERATIVE DESIGN AND ENERGY OPTIMIZATION PLATFORM: A QUALITATIVE CASE STUDY OF PAMPANGA STATE UNIVERSITY
Keywords:
Net Zero Energy Building, AI Generative Design, Energy Optimization Platform, Sustainability Parameters, Pampanga State UniversityAbstract
Educational buildings are among the largest consumers of energy and contributors to carbon emissions in the Philippines, yet no state college or university in the country has fully adopted Net Zero Energy Building (NZEB) performance. This study aimed to develop sustainability parameters for transforming the existing buildings of Pampanga State University (PSU) into near-net-zero energy buildings through the integration of artificial intelligence (AI)-driven generative design and energy optimization platforms. Using a qualitative, simulation-driven, design-based research approach, building plans, material data, climate conditions, operational schedules, and estimated electricity consumption were evaluated using six digital platforms — Ecoify, One Click LCA Planetary, the Embodied Carbon in Construction Calculator (EC3), Build Carbon Neutral, EDGE, and OpenStudio, supplemented by the CBE Clima Tool for climate analysis. AI-assisted generative design and energy-optimization simulations were then applied to test passive and active interventions, including shading, natural ventilation, envelope improvement, efficient lighting and cooling, smart controls, landscape enhancement, and solar photovoltaic integration. Results showed that administrative air-conditioning accounted for 53.43% of the representative building's estimated monthly energy consumption, followed by classroom fans (28.50%), lighting (9.50%), and office equipment (8.56%). The campus's estimated embodied carbon footprint was approximately 14,639 metric tons of CO₂, driven primarily by reinforced-concrete construction. Comparative material and design-scenario simulations indicated that low-carbon and optimized interventions could reduce embodied carbon by 21–62% and operational Energy Use Intensity (EUI) by up to 56% (from 210 to 92 kWh/m²/year) relative to baseline conditions. On the strength of these findings, the study proposes a five-phase Sustainability Parameter — Assessment and Baseline Analysis, AI-Driven Generative Design Optimization, Energy Simulation and Validation, Sustainable Design Implementation, and Monitoring and Continuous Improvement — to guide PSU and comparable institutions toward NZEB performance. The study concludes that AI-driven design methodologies can support performance-based, evidence-driven decision-making for sustainable campus planning, while noting that its findings rest on simulation rather than post-occupancy measurement and should be read alongside the data-verification notes in Section 3.4 and the Editorial and Citation Integrity Notice preceding the reference list.
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