Rachmatullah Muhammad Ibnu Choldun, Santoso Judhi, Surendro Kridanto
School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, West Java, Indonesia.
PeerJ Comput Sci. 2021 Sep 20;7:e724. doi: 10.7717/peerj-cs.724. eCollection 2021.
Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis.
人工神经网络（ANN）是人工智能技术之一，已被广泛应用于包括风速预测在内的许多领域以进行预测。本研究的目的是确定用于预测风速的神经网络拓扑结构。拓扑结构确定意味着找到神经网络中隐藏层的数量以及相应隐藏层的隐藏神经元数量。本研究与先前研究的不同之处在于，本研究的目标函数是回归，而先前研究的目标函数是分类。使用主成分分析（PCA）和K均值聚类来确定神经网络的拓扑结构。PCA用于确定隐藏层的数量，而聚类用于确定相应隐藏层的隐藏神经元数量。然后使用所选的拓扑结构来预测风速。接着将使用PCA和聚类进行拓扑结构确定的性能与其他几种方法进行比较。实验结果表明，使用PCA和聚类确定的神经网络拓扑结构的性能优于所比较的其他方法。性能是根据均方根误差（RMSE）值来确定的，RMSE值越小，神经网络的性能越好。在未来的研究中，在进行PCA和聚类分析之前，有必要应用输入属性和输出属性之间的相关性或关系，然后进行分析。
