Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

2021 ◽  
Author(s):  
Deepa Subramaniam Nachimuthu ◽  
Abhik Banerjee ◽  
Jayakumar Karuppaiah
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Network Models ◽  
Neural Network Models ◽  
Wind Power Forecasting ◽  
Power Forecasting

A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

2019 ◽  
Vol 250 ◽  
pp. 530-539 ◽  
Author(s):  
Ying-Yi Hong ◽  
Christian Lian Paulo P. Rioflorido
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Power Forecasting

Wind Power Forecasting Using Wavelet Decomposition and Elman Neural Network

2012 ◽  
Vol 608-609 ◽  
pp. 628-632
Author(s):  
Xiao Lu Gong ◽  
Zhi Jian Hu ◽  
Meng Lin Zhang ◽  
He Wang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Wavelet Decomposition ◽  
Weather Prediction ◽  
Neutral Network ◽  
Neural Network Models ◽  
Elman Neural Network ◽  
Wind Power Forecasting ◽  
Frequency Bands ◽  
Power Forecasting

The relevant data sequences provided by numerical weather prediction are decomposed into different frequency bands by using the wavelet decomposition for wind power forecasting. The Elman neural network models are established at different frequency bands respectively, then the output of different networks are combined to get the eventual prediction result. For comparison, Elman neutral network and BP neutral network are used to predict wind power directly. Several error indicators are given to evaluate prediction results of the three methods. The simulation results show that the Elman neural network can achieve good results and that prediction accuracy can be further improved by using the wavelet decomposition simultaneously.

Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models

2006 ◽  
Vol 21 (1) ◽  
pp. 273-284 ◽  
Author(s):  
T.G. Barbounis ◽  
J.B. Theocharis ◽  
M.C. Alexiadis ◽  
P.S. Dokopoulos
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Power Forecasting

scholarly journals A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7878
Author(s):  
Saira Al-Zadjali ◽  
Ahmed Al Maashri ◽  
Amer Al-Hinai ◽  
Rashid Al Abri ◽  
Swaroop Gajare ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
Energy Management Systems ◽  
Proposed Model ◽  
Power Utilities

To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan.

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

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