This work addresses the performance comparison between four clustering
techniques with the objective of achieving strong hybrid models in supervised
learning tasks. A real dataset from a bio-climatic house named Sotavento placed
on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has
been collected. Authors have chosen the thermal solar generation system in
order to study how works applying several cluster methods followed by a
regression technique to predict the output temperature of the system. With the
objective of defining the quality of each clustering method two possible
solutions have been implemented. The first one is based on three unsupervised
learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin) while the
second one, employs the most common error measurements for a regression
algorithm such as Multi Layer Perceptron.

通过比较四种聚类技术的性能，本研究旨在实现在监督学习任务中强大的混合模型。使用来自西班牙加利西亚卢戈 Xermade 的一个实验性风电场上的生态气候房屋 Sotavento 的实际数据集进行研究，选择了热太阳能发电系统，应用多种聚类方法和回归技术来预测系统的输出温度，并采用了三个无监督学习度量标准（Silhouette、Calinski-Harabasz 和 Davies-Bouldin）和多层感知器等回归算法的常见误差度量标准来定义每种聚类方法的质量。