Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Abstract

A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks

“…Therefore, finding a method to predict the compressed liquid density directly is a good way to estimate the numerical values without tedious experiments. To provide a convenient methodology for predictions, a comparative study among different possible models is necessary [26,27,34,35]. Here, we used the Song and Mason equation, SVM, and ANNs to develop theoretical and machine learning models, respectively, for predicting the compressed liquid densities of R227ea.…”

“…In this case study, support vector machine (SVM) and artificial neural networks (ANNs) were developed, respectively, in order to find out the best model for density prediction. ANNs are powerful non-linear fitting methods that developed during decades, which have good prediction results in many environmental related fields [26][27][28][29][30]. However, although ANNs usually give effective prediction performances, there is a risk of over-fitting phenomenon [26] if the best number of hidden nodes are not defined, which also indicates that the data size for model training should be large enough.…”

“…Alternatively, SVM, a new machine learning technique developed during these years, has been proved to be effective in numerical predictions for environmental fields [26,27]. The SVM is usually considered to have better generalization performance, leading to better predicted results in many scientific cases [26]. Furthermore, a proper training of SVM has fewer requirements to the data size, ensuring that it can be used for dealing with many complicated issues.…”

“…Additionally, the training of ANNs may require relatively long training times if the numbers of hidden nodes are high or the data size is large. Alternatively, SVM, a new machine learning technique developed during these years, has been proved to be effective in numerical predictions for environmental fields [26,27]. The SVM is usually considered to have better generalization performance, leading to better predicted results in many scientific cases [26].…”

Periodic fluctuations in correlation-based connectivity density time series: Application to wind speed-monitoring network in Switzerland

“…The multitude of studies on wind speed was focused on the analysis of wind speed as single time series, to which different statistical techniques were applied, such as distributional analysis [19], data mining [20], non-linear data driven models based on machine learning algorithms [21,22], fractal analysis [23,24], multifractal analysis [25]. Up to our knowledge, no network-based analysis has been performed on wind speed field so far.…”