scholarly journals Boosting Wavelet Neural Networks Using Evolutionary Algorithms for Short-Term Wind Speed Time Series Forecasting

2019 ◽  
pp. 15-26
Author(s):  
Hua-Liang Wei
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wind Speed ◽  
Evolutionary Algorithms ◽  
Time Series Forecasting ◽  
Wavelet Neural Networks ◽  
Short Term

Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand

2018 ◽  
Vol 203 ◽  
pp. 174-189 ◽  
Author(s):  
Weslly Puchalsky ◽  
Gabriel Trierweiler Ribeiro ◽  
Claudimar Pereira da Veiga ◽  
Roberto Zanetti Freire ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Forecasting ◽  
Perishable Products ◽  
Wavelet Neural Networks ◽  
Metaheuristic Optimization

Multi-step ahead time-series wind speed forecasting for smart-grid application

2021 ◽  
pp. 1-14 ◽  
Author(s):  
Hasmat Malik ◽  
Tahir Khursheed ◽  
Abdulaziz Almutairi ◽  
Majed A. Alotaibi
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wind Speed ◽  
Smart Grid ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
One Step ◽  
Grid Application ◽  
Daily Forecast ◽  
Intelligent Approach

In this paper, an intelligent approach for short-term wind speed forecasting (STWSF) is proposed. The STWSF models are developed to forecast the wind speed into a multi-step ahead forecasting, which is used to demonstrate the daily forecast results in One-Step-Ahead (OSA), Two-Step-Ahead (TSA), and Three-Step-Ahead (ThSA) based forecasting manner. To demonstrate the performance and results of the proposed approach, the real-site logged dataset is used for training and testing phase of the year 2015 to 2017. The STWSF is achieved recursively by utilizing the forecasted data in step-1 (OSA) as an input to generate the next forecasting data (in step-2 TSA) and the process is achieved upto level of step-3 (ThSA) forecasting. In order to results demonstration of fair adoptability of the proposed approach, different neural networks (NNs) models are developed for the same dataset, which shows that the proposed STWSF approach is outperformed and can be utilized for other locations for future applications.

Time Series Forecasting of Cyanobacteria Blooms in the Crestuma Reservoir (Douro River, Portugal) Using Artificial Neural Networks

2006 ◽  
Vol 38 (2) ◽  
pp. 227-237 ◽  
Author(s):  
Luis Oliva Teles ◽  
Vitor Vasconcelos ◽  
Luis Oliva Teles ◽  
Elisa Pereira ◽  
Martin Saker ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Forecasting ◽  
Cyanobacteria Blooms ◽  
Artificial Neural ◽  
Douro River
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