In the past decade, the construction speed of high-rise office towers worldwide has exhibited explosive growth. The unique morphological characteristics of high-rise office towers result in higher shape factors and relatively larger thermal loads. The traditional workflow of “design-evaluation” in the early stages of design imposes constraints on the diversity of tower morphology, the timeliness of performance evaluation, and the efficient integration of systems. Therefore, targeting the geometric characteristics of high-rise office towers, a systematically developed and universally applicable design grammar, named “Vertex-Based Polygonal Generative Grammar (VPGG)” is proposed. Additionally, a corresponding early-stage performance driven High-rise Office Tower Generative Design Framework (HOT_GDF) is introduced. Case study results demonstrate that, with the support of Artificial Neural Network, utilizing this system can not only globally explore the diversity of tower morphologies but also efficiently uncover greater energy-saving potential in complex architectural forms compared to simpler cubic forms, with an improvement of up to 7.76% during the early stages of design. Designed from the perspective of architects, the framework achieves logical, refined, and visual real-time interaction between computers and human minds during the early stages of tower design. This enhances design efficiency and facilitates design decision-making. It systematically integrates considerations for environmental performance, such as thermal load and thermal comfort, into the design process. Furthermore, it couples various aspects of morphological design with corresponding building performance, helping users in making design decisions from a rational and quantifiable perspective. This captures greater design potential, encompassing both form and performance, for high-rise office towers during the initial design phase.
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