scholarly journals Wind Speed Forecasting using Regression, Time Series and Neural Network Models: A Case Study of Suva

2020 ◽  
Author(s):  
Arieni Arzu ◽  
M. Rafiuddin Ahmed ◽  
M.G.M. Khan
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Network Models ◽  
Neural Network Models ◽  
Wind Speed Forecasting

Wind Speed Forecasting Using ARMA and Neural Network Models

2021 ◽  
Author(s):  
Uzair Zaman ◽  
Hamid Teimourzadeh ◽  
Elias Hassani Sangani ◽  
Xiaodong Liang ◽  
Chi Yung Chung
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Network Models ◽  
Neural Network Models ◽  
Wind Speed Forecasting

scholarly journals Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

2018 ◽  
Vol 10 (12) ◽  
pp. 4601 ◽  
Author(s):  
Yuewei Liu ◽  
Shenghui Zhang ◽  
Xuejun Chen ◽  
Jianzhou Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Wind Farm ◽  
Linear Models ◽  
Search Algorithm ◽  
Forecasting Model ◽  
Neural Network Models ◽  
Wind Speed Forecasting ◽  
Model Based

The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, it is found that the time series of wind speed demonstrate not only linear features but also nonlinear features. Hence, a combined forecasting model based on an improved cuckoo search algorithm optimizes weight, and several single models—linear model, hybrid nonlinear neural network, and fuzzy forecasting model—are developed in this paper to provide more trend change for time series of wind speed forecasting besides improving the forecasting accuracy. Furthermore, the effectiveness of the proposed model is proved by wind speed data from four wind farm sites and the results are more reliable and accurate than comparison models.

scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

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