Buildings consume a huge amount of energy, resulting in a considerable impact on the environment. In Canada, almost 70% of the total energy used by the commercial and institutional sectors was consumed by Heating, Ventilation and Air-Conditioning (HVAC) and lighting systems, which makes them the main targets of energy performance optimization methods. Furthermore, based on a governmental report, 40% of Quebec university buildings are in poor or very poor shape regarding structure and materials, and require immediate renovation. Therefore, it is of utmost importance to reduce energy consumption, and this can be accomplished by improving the design of new buildings or by renovating existing ones. Moreover, Simulation-Based Multi-Objective Optimization (SBMO) models can be used for optimizing and assessing different renovation scenarios considering Total Energy Consumption (TEC) and Life Cycle Cost (LCC). The time-consuming nature of SBMO has triggered the development of simplified and surrogate models within the design process. This study proposes a generative deep learning building energy model using Variational Autoencoders (VAEs), which could potentially overcome the current limitations. The proposed VAEs extract deep features from a whole building renovation dataset and generate renovation scenarios considering TEC and LCC of the existing institutional buildings. The proposed model also has the generalization ability due to its potential to reuse the dataset from a specific case in similar situations. The performance of the developed model has been demonstrated using a simulated renovation dataset to prove its potential. The results show that using generative VAEs is acceptable considering computational time and accuracy.

建筑物消耗大量能源，对环境造成很大影响。在加拿大，供暖，通风和空调（HVAC）和照明系统消耗了商业和机构部门使用的总能源的近70％，这使其成为能源绩效优化方法的主要目标。此外，根据政府报告，魁北克40％的大学建筑物在结构和材料上都处于劣等或极劣的状态，需要立即进行翻新。因此，降低能耗至关重要，这可以通过改进新建筑的设计或翻新现有建筑来实现。此外，基于仿真的多目标优化（SBMO）模型可用于优化和评估考虑到总能耗（TEC）和生命周期成本（LCC）的不同翻新方案。SBMO的耗时性质触发了设计过程中简化和替代模型的开发。这项研究提出了一种使用变分自动编码器（VAE）的生成型深度学习建筑能耗模型，该模型可以潜在地克服当前的局限性。拟议的VAE从整个建筑物翻新数据集中提取深层特征，并考虑现有机构建筑物的TEC和LCC来生成翻新方案。提出的模型还具有泛化能力，因为它具有在相似情况下重用特定案例中的数据集的潜力。已使用模拟的翻新数据集证明了开发模型的性能，以证明其潜力。结果表明，考虑到计算时间和准确性，使用生成型VAE是可以接受的。