The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network

Abstract

Artificial Neural Networks is the most used machine learning approach today. It is a very successful method in terms of accuracy and reliability. It is widely used in classification and estimation calculations. In order to achieve the desired performance a model created with ANN, a series of processes such as selection of network structure, learning algorithms, input and output values adjustment and transfer functions determination needs to be implemented in a sensitive manner. Multilayer Feedforward Backpropagation Network, which is used most frequently in supervised learning approaches, was considered in this study. The effect on the prediction performance of the developed model was investigated by using different statistical normalization methods on the data to be used in the network. For this purpose, 4-input 1-output artificial neural networks model were operated with wind-based data taken from Osmaniye Korkut Ata University measuring station. Wind speed, Wind Direction, Humidity and Density data are defined as input values while wind power was defined as output value. Input and output data are calculated with different normalization methods and more than one network models are designed with calculated values. As a result, the study showed that artificial neural networks model which is established by sigmoid normalization method has the best performance value.

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

“…The estimated results of the data groups were compared by using statistical error measurement and analysis methods including SMAPE, MAE, RMSE, and R 2 to evaluate the training and testing estimation performance of the developed models. The closer the value between the measured and estimated in statistical error measurement methods is to 0 and the closer to 1 in analysis methods, the estimation accuracy of the developed models is higher [40]. The flowchart of the HAGSR estimation processes of both provinces is given in Figure 7.…”

“…The normalization formula applied is given in equation (1). In this formula, each input (x i ) value was normalized (X n ) linearly between the 0 and 1 range by finding the minimum (x min ) and maximum (x max ) values of the raw dataset [40].…”

Telekomünikasyon Sektörü için Veri Madenciliği ve Makine Öğrenmesi Teknikleri ile Ayrılan Müşteri Analizi

“…Min-Max normalizasyon yöntemi, ham veri kümesinin minimum ve maksimum değerlerini bulur ve Denklem (1)'de verilen formüle göre her bir giriş değerini 0 ve 1 aralığında doğrusal olarak normalleştirir. Min-Max normalleştirme yöntemiyle ilgili temel sorun, minimum ve maksimum hesaplamada kullanılan örnek olmayan veri kümesinin değerleri bilinmemektedir [13].…”

Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden

“…Research on data normalization techniques as a stage of data preprocessing in NNBP has been widely carried out [4][5][6][7][8][9][10][11][12]. So, it can be known that normalization techniques include several data normalization techniques, namely, Z-score, min-max, mean-MAD, median-MAD, sigmoid, decimal scaling, tanh estimator, vector, and softmax.…”