PSO-NN-Based Hybrid Model for Long-Term Wind Speed Prediction: A Study on 67 Cities of India

2018 ◽  
pp. 319-327 ◽  
Author(s):  
Hasmat Malik ◽  
Vinoop Padmanabhan ◽  
R. Sharma
Keyword(s):  
Wind Speed ◽  
Hybrid Model ◽  
Wind Speed Prediction ◽  
Speed Prediction

Wind Speed Forecasting Using a Combined Method Based on Auto Regression and Wavelet Transform

2012 ◽  
Vol 512-515 ◽  
pp. 803-808
Author(s):  
Ji Long Tong ◽  
Zeng Bao Zhao ◽  
Wen Yu Zhang
Keyword(s):  
Wind Speed ◽  
Original Data ◽  
Ar Model ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Prediction Time ◽  
New Strategy ◽  
Speed Prediction ◽  
Auto Regression

This paper presents a new strategy in wind speed prediction based on AR model and wavelet transform.The model uses the adjacent data for short-term wind speed forecasting and the data of the same moment in earlier days for long-term wind speed prediction at that moment,taking the similarity of wind speed at the same moment every day into account.Using the new model to analyze the wind speed of An-xi,China in April,2010,this paper concludes that the model is effective for that the correlation coefficient between the predicted value and the original data is larger than 0.8 when the prediction is less than 48 hours;while the prediction time is long ahead (48-120h),the error is acceptable (within 40%),which demonstrates that the new method is a novel and good idea for prediction on wind speed.

scholarly journals Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Aiqing Kang ◽  
Qingxiong Tan ◽  
Xiaohui Yuan ◽  
Xiaohui Lei ◽  
Yanbin Yuan
Keyword(s):  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Hybrid Model ◽  
Superposition Principle ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Mode Decomposition ◽  
Speed Prediction

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

scholarly journals LONG TERM WIND SPEED PREDICTION USING WAVELET COEFFICIENTS AND SOFT COMPUTING

2016 ◽  
Vol 07 (01) ◽  
pp. 1338-1343
Author(s):  
Manju Khanna ◽  
◽  
Srinath N.K. ◽  
Mendiratta K. ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Soft Computing ◽  
Wavelet Coefficients ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm

2020 ◽  
Vol 6 ◽  
pp. 1147-1159 ◽  
Author(s):  
Saeed Samadianfard ◽  
Sajjad Hashemi ◽  
Katayoun Kargar ◽  
Mojtaba Izadyar ◽  
Ali Mostafaeipour ◽  
...  
Keyword(s):  
Wind Speed ◽  
Hybrid Model ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Multi Layer Perceptron ◽  
Wind Speed Prediction ◽  
Whale Optimization ◽  
Speed Prediction
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