Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

Palomares-Salas, José Carlos; Agüera-Pérez, Agustín; Rosa, Juan José González de la; Sierra-Fernández, José María; Moreno-Muñoz, Antonio

doi:10.3390/en6115807

Cited by 9 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of neurons in each layer is reported in Table 3. After an optimization process oriented to minimize the Mean Square Error, it was verified that for the hidden layer (layer 2) the best value corresponds to the mean of the neurons between the input and output layer [43]. In the first layer the hyperbolic tangent sigmoid transfer function (TANSIG) [44] was applied and in the second layer the linear transfer function (PURELIN) [45] was used.…”

Section: The Artificial Neural Network Methodsmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

Section: The Artificial Neural Network Methodsmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The empirically retrieved 10-hour time-window interval was found to be a convenient trade-off to the model, which comprises sufficient information diversity and low cross-correlation between Train and Test datasets. Our choice is also supported by existing studies on the auto-correlation decay in wind speed time series [24].…”

Section: Train-test-validation Strategymentioning

confidence: 91%

A Machine Learning Approach to Correct Turbulence Intensity measured by Floating Lidars

Rapisardi,

Araújo Da Silva,

Miquel

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In this work we introduce a supervised Machine Learning (ML) model to correct the Turbulence Intensity (TI) measured by Floating LiDAR Systems (FLS) in offshore environment. The model was developed using data from 46 EOLOS-FLS200 validation campaigns (≈ 4.6 years) carried out at three reference sites in the North Sea. It is based on Gradient Boosting Decision Tree (GBDT) and accounts for wind characteristics, atmospheric conditions, buoy motion, and wave features. Numerical analyses pronounced a consistent improvement in both coefficient of determination (R 2) and Mean Bias Error yielded by the ML-corrected TI. In addition, TI estimates in accordance with the state-of-the-art best practices were successfully obtained, even when evaluating the ML model in a site out of the training dataset, which demonstrates the model’s robustness.

show abstract

“…The equation (8) presented in the GA is also adopted as the fitness function in the MEA. The other detailed parameters of the MEA searching are listed as follows:…”

Section: Mlp Neural Network Optimized By Meamentioning

confidence: 99%

“…Their experimental results showed that the RT based forecasting methods were able to obtain very satisfactory results. Palomares-Salas et al [8] compared eight models for the wind speed one-hour ahead forecasting. In the study, the PM (Persistent Model), the ARIMA model, the MLR (Multiple Linear Regression) and five different types of neural networks were estimated.…”

Section: Introductionmentioning

confidence: 99%

New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks

et al. 2015

View full text Add to dashboard Cite

a b s t r a c tWind speed high-precision prediction is one of the most important technical aspects to protect the safety of wind power utilization. In this study, two new hybrid methods [FEEMD-MEA-MLP/FEEMD-GA-MLP] are proposed for the wind speed accurate multi-step predictions by combining FEEMD (Fast Ensemble Empirical Mode Decomposition), MEA (Mind Evolutionary Algorithm), GA (Genetic Algorithm) and MLP (Multi Layer Perceptron) neural networks. In these two hybrid methods, the FEEMD algorithm is adopted to decompose the original wind speed series into a number of sub-layers and the MLP neural networks optimized by the MEA algorithm and the GA algorithm are built to predict the decomposed wind speed sub-layers, respectively. The innovation of the study is to investigate the promoted percentages of the MLP neural networks by the FEEMD decomposition and the MEA/GA optimization, respectively. The involved forecasting models in the performance comparison in the study include the hybrid FEEMD-MEA-MLP, the hybrid FEEMD -GA-MLP, the hybrid FEEMD-MLP, the hybrid MEA-MLP, the hybrid GA-MLP and the single MLP. Two experimental results show that: (a) among all the involved methods, the hybrid FEEMD-MEA-MLP model has the best forecasting performance; (b) the FEEMD algorithm promotes the performance of the MLP neural networks significantly while the MEA/GA algorithms do not improve the performance of the MLP neural networks significantly; and (c) the hybrid FEEMD-MEA-MLP method and the hybrid FEEMD-GA-MLP method are both effective in the wind speed high-precision predictions.

show abstract

Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

Cited by 9 publications

References 31 publications

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

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

A Machine Learning Approach to Correct Turbulence Intensity measured by Floating Lidars

New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks

Contact Info

Product

Resources

About