Several equitable approaches have been proposed to reduce world energy consumption against a backdrop of a growing global climate crisis. Among these, we can mention the attempts to improve the energy use of household appliances and utilities, such as air conditioners. One of the strategies used to reduce these devices’ unnecessary energy consumption is estimating the thermal variation in the environments, especially still during their design phase. One of the most advanced methods for this estimation uses computer simulations, which require a high level of technical knowledge. For that, a relatively simple alternative is the creation of metamodels. This work compares two machine learning approaches for developing a metamodel capable of estimating the thermal load in single-family buildings. The metamodels evaluated were the Artificial Neural Networks and the Gradient Boosting Machine. The results obtained made it possible to observe a better performance in the Gradient Boosting Machine approach indicators in relation to Artificial Neural Networks. The negative point is that Gradient Boosting Machine requires a relatively long training time, making its use in routine projects less feasible.

在全球气候危机日益严重的背景下，已经提出了几种公平的方法来减少世界能源消耗。其中，我们可以提及改善家用电器和公用事业（例如空调）的能源使用的尝试。用于减少这些设备不必要的能源消耗的策略之一是估计环境中的热变化，尤其是在它们的设计阶段。这种估计的最先进方法之一是使用计算机模拟，这需要高水平的技术知识。为此，一个相对简单的替代方法是创建元模型。这项工作比较了两种机器学习方法，用于开发能够估计单户建筑热负荷的元模型。评估的元模型是人工神经网络和梯度提升机。获得的结果使我们可以观察到与人工神经网络相关的梯度提升机方法指标的更好性能。不利的一点是 Gradient Boosting Machine 需要相对较长的训练时间，使其在常规项目中的使用不太可行。