PEMANFAATAN GENERATIVE DESIGN DALAM OPTIMASI KINERJA ENERGI BANGUNAN

TINJAUAN LITERATUR SISTEMATIS: PEMANFAATAN GENERATIVE DESIGN DALAM OPTIMASI KINERJA ENERGI BANGUNAN The building sector accounts for approximately 30–40% of global final energy consumption and nearly 40% of energy-related CO₂ emissions, making it a critical target for decarbonization efforts. Generative Design (GD), an emerging computational paradigm leveraging parametric modeling, multi-objective optimization algorithms, and machine learning, offers transformative potential for optimizing building performance from the earliest conceptual stages. This paper presents a systematic literature review (SLR) of 47 peer-reviewed publications (2019–2025) indexed in Scopus, examining how generative design methods are applied to optimize building energy consumption, daylighting, and thermal comfort. Using the PRISMA framework, we evaluate publication trends, journal quality, methodological taxonomies, optimization algorithms, simulation engines, and climate contexts. The findings reveal a dramatic acceleration in research output, with 57.4% of key studies published in 2024–2025 and 68.1% featured in Q1 Scopus journals. The Rhino/Grasshopper ecosystem combined with Ladybug Tools and EnergyPlus dominates current practice (87.2%), while Multi-Objective Genetic Algorithms (MOGA/NSGA-II) remain the most widely adopted optimization solver (76.6%). Furthermore, Generative AI (GANs, CycleGAN, SolarGAN) and Machine Learning surrogate models (17.0%) are rapidly transforming the field by accelerating physics-based simulation speeds up to several hundred times. Crucially, a major geographic disparity is identified: only 8.5% of studies focus on tropical/subtropical climates. This review establishes an integrative framework for performance-driven generative design and highlights future directions for tropical climate adaptation, explainable AI, and local green building standard integration.