The aim of this study is to propose a reliable method to optimize the energy consumption of buildings. Also, the most effective input parameters are defined which are used in the energy consumption of a research center building located in Iran as a case study. Accordingly, EnergyPlus software is implemented to evaluate energy consumption and scrutinize the crucial factors numerically. Afterward, a robust artificial neural network (ANN) using multi-layer perceptron model (MLP) is created, trained, and tested to simulate energy consumption in the building. Furthermore, energy optimization is performed by Galapagos plugin based on a Genetic Algorithm considering the critical variables. The main results show that the optimization of the system can mitigate energy consumption by about 35 %. In addition, the outcomes of the sensitivity analysis demonstrate that the number of occupants has the highest influence on the energy consumption of the edifice followed by wall U-value which is related to wall insulation. Finally, the results of computations showed that the trained MLP model proposed in this study can accurately predict energy consumption in the building. To sum up, the proposed model may be applied to similar buildings to predict and optimize energy consumption.

这项研究的目的是提出一种可靠的方法来优化建筑物的能耗。此外，还定义了最有效的输入参数，这些参数将用于案例研究中位于伊朗的研究中心大楼的能耗中。因此，实施EnergyPlus软件以评估能耗并从数字上审查关键因素。然后，使用多层感知器模型（MLP）创建，训练和测试强大的人工神经网络（ANN），以模拟建筑物中的能耗。此外，加拉帕戈斯插件基于遗传算法，考虑了关键变量，对能量进行了优化。主要结果表明，系统的优化可以减少约35％的能耗。此外，敏感性分析的结果表明，居住人数对建筑物的能耗影响最大，其次是与墙壁隔热有关的墙壁U值。最后，计算结果表明，本研究提出的训练有素的MLP模型可以准确预测建筑物的能耗。综上所述，所提出的模型可以应用于相似的建筑物，以预测和优化能耗。