Stochastic Operation–Based Optimization of Office Building Envelope Thermal Performance
Achieving an effective balance between indoor thermal comfort and operational energy consumption is a core objective of building thermal performance design. Conventional envelope design methods typically rely on fixed occupancy and operation schedules, overlooking the inherent randomness of real building use. This limitation often leads to inaccurate performance estimations and suboptimal design decisions. This study addresses this gap by integrating stochastic building operation behavior into the thermal optimization design of office building envelopes.
Limitations of Deterministic Thermal Design Approaches
Traditional thermal design practices assume predefined schedules for air-conditioning use and window operation, which fail to capture the variability of occupant behavior and operational uncertainty. Such deterministic assumptions can distort predictions of heating and cooling loads, ultimately affecting indoor comfort and energy efficiency. Recognizing these limitations provides the foundation for adopting stochastic operation modeling as a more realistic design framework.
Stochastic Modeling of Building Operation Behavior
The study establishes a stochastic operation prediction model for office building air-conditioning systems and exterior windows. Annual hourly random operation sequences are generated to reflect realistic patterns of building use. These probabilistic schedules better represent occupant-driven behaviors, enabling more accurate simulations of building thermal performance under real-world operating conditions.
Performance Simulation and Neural Network Prediction
Based on the stochastic operation schedules, comprehensive building performance simulations are conducted to evaluate heating load, cooling load, and thermal comfort. To enhance computational efficiency, an artificial neural network is developed as a rapid prediction model. This surrogate model effectively captures nonlinear relationships between envelope parameters, operational randomness, and thermal performance indicators.
Multi-Objective Optimization Using Genetic Algorithms
To simultaneously improve thermal comfort and reduce heating and cooling demands, a multi-objective optimization framework is established using genetic algorithms. The optimization process explores envelope design variables under stochastic operation constraints, identifying optimal trade-offs between energy efficiency and occupant comfort rather than relying on single-objective performance metrics.
Case Study and Design Implications for Hot-Summer–Cold-Winter Regions
Applying the proposed method to office buildings in China’s hot-summer–cold-winter climate zone, the study demonstrates how stochastic operation-aware design strategies can lead to more robust and reliable envelope solutions. The results highlight practical optimization pathways for low-energy building envelopes that maintain comfortable thermal environments under uncertain operational conditions, offering valuable guidance for future building thermal design.
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