Method of Wind Power Prediction Based on ANN and Improvements

2014 ◽  
Vol 521 ◽  
pp. 143-146 ◽  
Author(s):  
Shao Shuai Song ◽  
Ran Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Good Effect ◽  
Power Prediction ◽  
Learning Speed ◽  
Wind Power Prediction ◽  
Artificial Neural Network Ann

This paper puts emphasis on studying on artificial neural network (ANN) method. Following that a model of wind power prediction is established based on back-propagation neural network. In order to improve the learning speed of ANN, a revised BP algorithm is adopted by using variable step and the combination of GA and BP algorithm. This method has a good effect in practice.

The Effect of Hyper-Parameters on the Performance of Third Order Neural Network Algorithms on Medical Classification Data

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Nazri Mohd Nawi ◽  
◽  
Prihastuti Harsani ◽  
Eneng Tita Tosida ◽  
Khairina Mohamad Roslan ◽  
...  
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Numerical Data ◽  
Bp Algorithm ◽  
Back Propagation Algorithm ◽  
Classification Problems ◽  
Third Order ◽  
Learning Speed ◽  
Medical Classification ◽  
Artificial Neural Network Ann

The artificial neural network (ANN) particularly back propagation (BP) algorithm has recently been applied in many areas. It is known that BP is an excellent classifier for nonlinear input and output numerical data. However, the popularity of BP comes with some drawbacks such as slow in learning and easily getting stuck in local minima. Improving training efficiency of BP algorithm is an active area of research and numerous papers have been reviewed in the literature. Furthermore, the performance of BP algorithm also highly influenced by the size of the datasets and the data preprocessing techniques that been chosen. This paper presents an improvement of BP by adjusting the two term parameters on the performance of third order neural network methods. This work also demonstrates the advantages of using preprocessing dataset in order to improve the BP convergence. The efficiency of the proposed method is verified by means of simulation on medical classification problems. The results show that the proposed implementation significantly improves the learning speed of the general back-propagation algorithm.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

2019 ◽  
Vol 53 (6) ◽  
pp. 27-34
Author(s):  
Tim Chen ◽  
C.Y.J. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Variation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Systems ◽  
Modeling And Forecasting ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

AbstractThe reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.

Short-term wind power prediction based on improved small-world neural network

2018 ◽  
Vol 31 (7) ◽  
pp. 3173-3185 ◽  
Author(s):  
Shuang-Xin Wang ◽  
Meng Li ◽  
Long Zhao ◽  
Chen Jin
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Small World ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction
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