Turbine Location Wind Speed Forecast Using Convolutional Neural Network

2019 ◽  
Author(s):  
Tianhu Wan ◽  
Hua Li ◽  
Chen Wang ◽  
Peng Kou
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Convolutional Neural Network ◽  
Wind Speed Forecast

Wind Speed Forecast for Wind Farms Based on Phase Space Reconstruction of Wavelet Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 840-845 ◽  
Author(s):  
Xiao Bing Xu ◽  
Jun He ◽  
Jian Ping Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Phase Space ◽  
Wind Speed ◽  
Network Model ◽  
Neural Network Model ◽  
Phase Space Reconstruction ◽  
Wavelet Neural Network ◽  
Non Linear ◽  
Wind Speed Forecast

Wind speed forecast is a non-linear and non-smooth problem. nonlinear and non-stationary are two kinds of mathematical problem, it is difficult to model with a single method, so that, a wavelet neural network model is set, the non-linear process of wind speed is forecast by neural networks and the non-stationary process of wind speed is decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transforms. wavelet combined with neural network model avoid the neural network model that can not handle non-stationary questions .while, the effect of indefinite inputs are removed by embedding dimension of phase space to determine neural networks inputs. The simulation results show that phase space reconstruction of wavelet neural network is more accuracy than the ordinary BP neural network. It could be well applied in wind speed forecasts.

scholarly journals Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Caifen He ◽  
Qiaote Chen ◽  
Xuyuan Fang ◽  
Yangzhang Zhou ◽  
Randi Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Convolutional Neural Network ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Deep Convolutional Neural Network ◽  
Percentage Error ◽  
Strong Wind ◽  
Wind Speed Forecasting ◽  
The Mean

Wind speed forecasting is an important issue in Marine fisheries. Improving the accuracy of wind speed forecasting is helpful to reduce the loss of fishery economy caused by strong wind. This paper proposes a wind speed forecasting method for fishing harbor anchorage based on a novel deep convolutional neural network. By combining the actual monitoring data of the automatic weather station with the numerical weather prediction (NWP) products, the proposed method constructing a deep convolutional neural network was based wind speed forecasting model. The model includes a one-dimensional convolution module (1D-CM) and a two-dimensional convolution module (2D-CM), in which 1D-CM extracts the time series features of the meteorological data, and 2D-CM is used to mine the latent semantic information from the outputs of 1D-CM. In order to alleviate the overfitting problem of the model, the L2 regularization and the dropout strategies are adopted in the training process, which improves the generalization of the model with higher reliability for wind speed prediction. Simulation experiments were carried out, using the 2016 wind speed and related meteorological data of a sheltered anchorage in Xiangshan, Ningbo, China. The results showed that, for wind speed forecast in the next 1 h, the proposed method outperform the traditional methods in terms of prediction accuracy; the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of the proposed method are 0.3945 m/s and 5.71%, respectively.

scholarly journals Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1772 ◽  
Author(s):  
Kumar Shivam ◽  
Jong-Chyuan Tzou ◽  
Shang-Chen Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Convolutional Neural Network ◽  
Real World ◽  
Prediction Models ◽  
Short Term Memory ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Speed Prediction

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

scholarly journals A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2292 ◽  
Author(s):  
Jianzhong Zhou ◽  
Han Liu ◽  
Yanhe Xu ◽  
Wei Jiang
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Speed ◽  
Convolutional Neural Network ◽  
Wind Power Generation ◽  
The Other ◽  
Variational Model ◽  
Model Decomposition ◽  
Wind Speed Forecasting ◽  
Hybrid Framework

Wind speed is an important factor in wind power generation. Wind speed forecasting is complicated due to its highly nonstationary character. Therefore, this paper presents a hybrid framework for the development of multi-step wind speed forecasting based on variational model decomposition and convolutional neural networks. In the first step of signal pre-processing, the variational model decomposition approach decomposes the wind speed data into several independent modes under different center pulsation. The vibrations of decomposed modes are useful for accurate wind speed forecasting. Then, the influence of different numbers of modes and the input length of the convolutional neural network are discussed to select the optimal value through calculating the errors. During the regression step, each mode is treated as a channel that constitutes the input of the forecasting model. The convolution operations in convolutional neural networks extract helpful local features in each mode and the relationships between modes for forecasting. We take advantage of the convolutional neural network and directly output multi-step forecasting results. In order to show the forecasting and generalization performance of the proposed method, wind seed data from two wind farms in Inner Mongolia, China and Sotavento Galicia, Spain with different statistical information were employed. Some classic statistical approaches were adopted for comparison. The experimental results show the satisfactory performance for all of the methods in single-step forecasting and the advantages of using decomposed modes. The root mean squared errors range from 0.79 m/s to 1.64 m/s for all of the methods. In the case of multi-step forecasting, our proposed method achieves an outstanding improvement compared with the other methods. The root mean squared error of our proposed method was 1.30 m/s while the worst performance of the other methods was 9.68 m/s. The proposed method is able to directly predict the variation trend of wind speed based on historical data with minor errors. Hence, the proposed forecasting schemes can be utilized for wind speed multi-step forecasting to cost-effectively manage wind power generation.

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