Simulation‐Based and Machine Learning Methodologies for Energy Optimization in Buildings

As modern buildings face increasing sustainability and performance demands, simulation‐based optimization and machine learning have become essential tools in the design process. This article highlights the limitations of traditional design methods and explores how multi‐objective optimization and surrogate modeling enable scalable, efficient, and data‐driven evaluation of building performance. The design paradigm has shifted from rule‐based approaches to intelligent, algorithm‐driven processes that balance energy, comfort, cost, and emissions. Simulation‐based optimization integrates dynamic simulation with advanced algorithms to explore complex design spaces and identify optimal trade‐offs. To this end, a case study of a low‐rise residential building in Patras, Greece, is presented using the Non‐dominated Sorting Genetic Algorithm II. The model evaluated 12 envelope‐related design variables and generated a well‐distributed Pareto front of 65 non‐dominated solutions, highlighting trade‐offs between heating energy demand and construction cost. The sensitivity patterns observed across the Pareto set showed that insulation thickness and glazing performance were the most influential drivers of heating demand. Machine learning–based surrogate modeling enhances optimization further by approximating computationally expensive simulations with fast, predictive models. Trained on sampled simulation data, these surrogates enable rapid optimization and sensitivity analysis. A second case study, referring to a school retrofit in Portugal, demonstrated that an Artificial Neural Network surrogate reduced computation time from approximately 75 days to just 3 days while maintaining high predictive accuracy. Sensitivity analysis indicated that window upgrades, HVAC efficiency, and solar thermal integration had the strongest influence on energy use and thermal discomfort. These quantitative and sensitivity‐based insights demonstrate how combining simulation‐based and machine learning methodologies supports high‐performance, cost‐effective, and environmentally responsible building design and retrofit. This article is categorized under: Sustainable Energy > Energy Efficiency Cities and Transportation > Buildings