Since cooling and heating loads are recognized as key characteristics for evaluating the energy efficiency of buildings, it appears indisputable that they must be predicted and analyzed for residential structures. Accordingly, the multi-layer perceptron neural network is applied for predicting the heating and cooling loads using the experimental dataset. The used dataset is obtained by monitoring the impact of the building's dimensions on energy consumption. To optimize the training process of the multi-layer perceptron neural network, several optimizers are employed. Besides, different statistical performance indicators are considered to see which selected optimizer outperforms the rest in terms of accuracy and authenticity. The obtained results emphasize the remarkable performance of adaptive chaotic grey wolf optimization, which can be used to train the multi-layer perceptron neural network and forecast the buildings’ energy consumption with the highest accuracy. According to the obtained results, the hybrid multi-layer perceptron neural network- adaptive chaotic grey wolf optimization method demonstrates the best performance. The optimum number of neurons in the hidden layer is obtained to be 15. Also, based on the statistical performance indicators, the selected method reveals an R² of 0.9123 and 0.9419 for cooling and heating loads, respectively.

由于冷却和加热负荷被认为是评估建筑物能源效率的关键特征，因此必须对住宅结构进行预测和分析似乎是无可争辩的。因此，多层感知器神经网络被应用于使用实验数据集预测热负荷和冷负荷。使用的数据集是通过监测建筑物尺寸对能源消耗的影响而获得的。为了优化多层感知器神经网络的训练过程，使用了几个优化器。此外，还考虑了不同的统计性能指标，以查看哪个优化器在准确性和真实性方面优于其他优化器。获得的结果强调了自适应混沌灰狼优化的显着性能，可用于训练多层感知器神经网络并以最高精度预测建筑物的能耗。根据所得结果，混合多层感知器神经网络-自适应混沌灰狼优化方法表现出最佳性能。隐藏层中的最佳神经元数量为 15。此外，基于统计性能指标，所选方法揭示了 R冷负荷和热负荷分别为 0.9123 和 0.9419 的^{2 。}