Short-term forecasting for wind speed based on wavelet decomposition and LMBP neural network

2011 ◽  
Author(s):  
Wei Wei ◽  
Guilian Wu ◽  
Minghai Yang ◽  
Yongwu Zhang ◽  
Shengxiao Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wavelet Decomposition ◽  
Short Term ◽  
Short Term Forecasting

Hybrid Neural Networks for Renewable Energy Forecasting

2021 ◽  
pp. 796-818
Author(s):  
Firuz Ahamed Nahid ◽  
Weerakorn Ongsakul ◽  
Nimal Madhu M. ◽  
Tanawat Laopaiboon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Renewable Energy ◽  
Wind Speed ◽  
Solar Radiation ◽  
Renewable Energy Sources ◽  
Energy Harvest ◽  
Short Term ◽  
Energy Forecasting ◽  
Short Term Forecasting

One of the key applications of AI algorithms in power sector involves forecasting of stochastic renewable energy sources. To manage the generation of electricity from solar or wind effectively, accurate forecasting models are imperative. In order to achieve this goal, a sophisticated hybrid neural network formulation is discussed here in this chapter. long-short-term memory and recurrent neural networks combination is formulated for very short-term forecasting of wind speed and solar radiation. In intervals of 15 and 30 minutes, time series forecasts are made that are ahead by multiple steps. For maximum energy harvest, both point wise and probabilistic forecasting approaches are combined. Historic data is collected for solar radiation, wind speed, temperature, and relative humidity, and are used to train the model. The proposed model is compared with convolutional and LSTM neural network models individually in terms of RMSE, MAPE, MAE, and correlation, and is identified to have better forecasting accuracy.

Hybrid Neural Networks for Renewable Energy Forecasting

2021 ◽  
pp. 200-222
Author(s):  
Firuz Ahamed Nahid ◽  
Weerakorn Ongsakul ◽  
Nimal Madhu M. ◽  
Tanawat Laopaiboon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Renewable Energy ◽  
Wind Speed ◽  
Solar Radiation ◽  
Renewable Energy Sources ◽  
Energy Harvest ◽  
Short Term ◽  
Energy Forecasting ◽  
Short Term Forecasting

One of the key applications of AI algorithms in power sector involves forecasting of stochastic renewable energy sources. To manage the generation of electricity from solar or wind effectively, accurate forecasting models are imperative. In order to achieve this goal, a sophisticated hybrid neural network formulation is discussed here in this chapter. long-short-term memory and recurrent neural networks combination is formulated for very short-term forecasting of wind speed and solar radiation. In intervals of 15 and 30 minutes, time series forecasts are made that are ahead by multiple steps. For maximum energy harvest, both point wise and probabilistic forecasting approaches are combined. Historic data is collected for solar radiation, wind speed, temperature, and relative humidity, and are used to train the model. The proposed model is compared with convolutional and LSTM neural network models individually in terms of RMSE, MAPE, MAE, and correlation, and is identified to have better forecasting accuracy.

scholarly journals Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Niya Chen ◽  
Zheng Qian ◽  
Xiaofeng Meng
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Southern China ◽  
Energy Utilization ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Hourly Data ◽  
Short Term Forecasting ◽  
Gp Model

Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm) is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error) compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

scholarly journals Short-term forecasting of soil temperature using artificial neural network

2014 ◽  
Vol 22 (3) ◽  
pp. 576-585 ◽  
Author(s):  
Hossein Tabari ◽  
P. Hosseinzadeh Talaee ◽  
Patrick Willems
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Short Term ◽  
Artificial Neural ◽  
Short Term Forecasting

scholarly journals Wavelet Neural Network Based Multiobjective Interval Prediction for Short-Term Wind Speed

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 63352-63365 ◽  
Author(s):  
Zhichao Shi ◽  
Hao Liang ◽  
Venkata Dinavahi
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wavelet Neural Network ◽  
Short Term ◽  
Interval Prediction
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