Wavelet neural network–based wind speed forecasting and application of shuffled frog leap algorithm for economic dispatch with prohibited zones incorporating wind power

2017 ◽  
Vol 42 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Ulagammai Meyyappan
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Economic Dispatch ◽  
Wavelet Neural Network ◽  
Wind Power Generation ◽  
Smooth Functions ◽  
Wind Speed Forecasting

Wind speed and wind power generation are characterized by their inherent variability and uncertainty. To overcome this drawback, an accurate prediction of wind speed is essential. The purpose of this article is to develop a hybrid wavelet neural network model for wind speed forecasting and thus, in turn, for wind power generation. The combined optimal economic scheduling of the wind generators and conventional generators has also been investigated in this article. This article proposes shuffled frog leap algorithm for solving economic dispatch problem in power systems. The non-linear characteristics of the generator such as prohibited operating zone and non-smooth functions are considered. The feasibility of the proposed algorithm is demonstrated for 5 units, 6 units and 15 units systems and it is compared with the existing solution techniques. The results show that the proposed algorithm is indeed capable of handling economic dispatch problems.

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

scholarly journals A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Rasel Sarkar ◽  
Sabariah Julai ◽  
Sazzad Hossain ◽  
Wen Tong Chong ◽  
Mahmudur Rahman
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Power Generation ◽  
Activation Functions ◽  
Wind Speed Forecasting ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network

Since wind power is directly influenced by wind speed, long-term wind speed forecasting (WSF) plays an important role for wind farm installation. WSF is essential for controlling, energy management and scheduled wind power generation in wind farm. The proposed investigation in this paper provides 30-days-ahead WSF. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) with different network settings have been used to facilitate the wind power generation. The essence of this study is that it compares the effect of activation functions (namely, tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. A set of wind speed data was collected from different meteorological stations in Malaysia, situated in Kuala Lumpur, Kuantan, and Melaka. The proposed activation functions tansig of NARNN and NARXNN resulted in promising outcomes in terms of very small error between actual and predicted wind speed as well as the comparison for the logsig transfer function results.

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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