Enterprise AI Analysis
AI-driven design optimization for sustainable buildings: A systematic review
This systematic review analyzes recent advancements in AI-driven surrogate models for sustainable building design optimization. It highlights AI's pivotal role in enhancing efficiency and precision across both design and operational phases, with a specific focus on surrogate models in the design stage. The review covers the entire optimization workflow, from data preparation and model selection to training, validation, and final design optimization. Key findings include the widespread use of Feed Forward Neural Networks (FFNNs), the importance of advanced sampling techniques, and the significant reduction in computational time offered by surrogate models. The paper also identifies critical challenges and opportunities, such as improving model reusability, enhancing wind and shape modeling, and fostering explainable AI in sustainable building practices.
Executive Impact & Key Metrics
Quantifying the transformative potential of AI in sustainable building design.
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Energy Reduction Potential
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Studies Included in Review
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EnergyPlus Usage Rate
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FFNN Usage Rate
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Model Development Only
Studies in this category focused exclusively on surrogate model development, covering all necessary steps from building to evaluating a surrogate model, but without applying it to design variable optimization. Key areas include DL-aided models (Mateusz et al. [63], He et al. [42]), data generation (Venkatraj et al. [44]), feature selection (Didwania et al. [64]), occupant behavior modeling (Li et al. [66]), and explainable AI (Barbaresi et al. [67]). These works introduce new dimensions to optimization, leveraging modern DL techniques and addressing specific modeling challenges, though often with high data and training demands.
Model Development with Performance Tuning
This section focuses on studies that develop a surrogate model and incorporate an additional performance tuning step to further optimize model accuracy. This typically involves optimizing the ML model's hyperparameters to achieve higher accuracy. Techniques such as Genetic Algorithms (Garcia et al. [48]), Bayesian Optimization (Han et al. [49]), and comparative studies (Cai et al. [50]) were commonly employed to enhance model performance before its application in design optimization.
Model Development and Design Optimization
This category comprises the majority of reviewed literature, where surrogate models are developed and then used to derive optimal building design parameters. Studies are classified by their specific focus: climate change considerations (Zou et al. [51]), ensemble models (Chen et al. [53]), metaheuristic optimization (Yu et al. [54]), systematic sampling (Zheng et al. [57]), Python frameworks (Hocine et al. [58]), renovation (Asadi et al. [41]), feature selection (Chen et al. [37]), passive energy elements (Gou et al. [33]), active energy elements (Magnier et al. [79]), and various case studies (Gossard [81] for residential, Zhao et al. [86] for high-rise offices, Wang et al. [87] for multi-building scenarios, and Ji et al. [36] for prefabricated houses).
Design Optimization with Tuned Model
These studies integrate surrogate models with performance tuning techniques to enhance accuracy, followed by their employment for design optimization. This involves a two-stage process where the ML model itself is optimized (e.g., hyperparameter tuning or adaptive sampling) before being used to derive optimal building design parameters. Examples include RL optimization (Pan et al. [92]), parameter optimization using GWO (Liu et al. [95]), and adaptive sampling (Bre et al. [100]) to iteratively refine the dataset and improve model performance efficiently.
Enterprise Process Flow: Core Process of AI-Driven Design Optimization
Identify Objectives & Variables
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Generate Parameter Combinations
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Design & Simulate Building Performance
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Develop Surrogate ML Model
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Optimize with Surrogate Model
The AI-driven design optimization process is systematically broken down, starting from objective identification and variable selection, through data generation and surrogate model development, culminating in optimal parameter derivation. This structured approach leverages ML to significantly reduce computational burden and accelerate the design cycle.
| Algorithm | Data Need | Training Complexity | Main Use Cases |
|---|---|---|---|
| FFNN | High | High | Complex nonlinear relationships |
| SVR | Moderate | Moderate | Small to medium datasets, simple nonlinear problems |
| CNN | Very High | Very High | Image data, spatiotemporal patterns |
| Tree-Based Models | Low | Low | Explainability, tabular data, handling categorical data |
Selecting the appropriate ML algorithm is crucial for effective surrogate modeling. This comparison highlights the trade-offs between data requirements, computational complexity, and the types of problems each algorithm is best suited for, guiding practitioners in their model selection.
Computational Efficiency Gains
0% Reduction in Simulation TimeSurrogate models offer a significant acceleration over traditional physics-based simulations. For instance, studies have shown a 99% reduction in evaluation time, transforming multi-minute simulations into near-instantaneous predictions. This drastic speed-up enables designers to explore a much larger design space within practical timeframes.
Real-World Application: Multi-Building Layout Optimization
Optimizing Urban Layouts for Enhanced Comfort
In a study by Wang et al. [87], surrogate models were utilized to optimize the layout of 12 structures on a Beijing site. The objective was to improve both indoor visual comfort and outdoor thermal performance, demonstrating the applicability of AI-driven optimization to complex urban planning scenarios.
Such large-scale optimizations, traditionally computationally prohibitive, become feasible with surrogate models, enabling more sustainable and human-centric urban designs.
Calculate Your Potential ROI
Estimate the financial and efficiency gains your enterprise could achieve with AI-driven optimization.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrating AI-driven design optimization into your enterprise.
Phase 1: Discovery & Strategy Alignment
Conduct a detailed assessment of current design workflows, identify key sustainability objectives, and align AI integration strategy with your business goals. This includes data readiness assessment and initial feasibility studies for surrogate modeling.
Phase 2: Pilot Program & Model Development
Implement a pilot AI-driven optimization project focusing on a specific building type or design challenge. Develop and train initial surrogate models using identified design variables and simulation data. Validate model accuracy and efficiency.
Phase 3: Scaling & Integration
Expand AI solutions across more design projects, integrate surrogate models with existing design tools (e.g., BIM platforms), and establish continuous monitoring and retraining protocols for models. Focus on developing reusable models and explainable AI practices.
Phase 4: Advanced Optimization & Innovation
Explore advanced AI techniques like adaptive sampling, reinforcement learning for design, and incorporating external factors (wind, urban microclimates). Drive innovation in building design through multi-objective and multi-scenario optimization capabilities.
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