Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.

中文翻译：

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通过敏感性分析和使用 AI 的经济洞察来构建性能优化


由于各个利益相关者的要求复杂且经常相互冲突，因此优化建筑设计以提高能源效率和居住者舒适度带来了重大挑战。因此，本研究进行了多方面的敏感性和经济影响分析，旨在通过实施机器学习技术来提高建筑在能源效率和居住者舒适度方面的性能。使用包含挪威 Tvedestrand 高中 12,000 次能源模拟运行的广泛数据集，采用了多个机器学习模型，其中多层感知器的性能优于其他模型。此外，还使用了几种敏感性分析方法来探索单个参数对建筑性能的影响。分析表明，通风率、房间深度、立面的 U 值和热量增加都会显着影响能源消耗。还进行了经济影响分析，以比较传统 HVAC 系统与建筑管理系统 （BMS） HVAC 解决方案的成本效益。随着时间的推移，BMS HVAC 系统的运营成本显著降低，投资成本平均约为 1200 挪威克朗 （NOK）/m2，每年的运营成本约为 150 NOK/m2。不同经济情景下的敏感性分析突出了 BMS HVAC 系统的经济可行性。本研究确定了平衡能源效率和热舒适度的最佳建筑参数，实现了 11.05 至 22.51 kWh/m2 之间的总能耗和零不适时间 （h > 26 °C）。 总之，这些发现为利益相关者提供了有价值的见解，使他们能够就可持续建筑设计和能源效率改进做出明智的决策，确保在各种条件下的技术可靠性和财务可行性，同时使用经过测试的工具。