A wind speed forecasting model based on artificial neural network and meteorological data

2016 ◽  
Author(s):  
Antonella R. Finamore ◽  
Vito Calderaro ◽  
Vincenzo Galdi ◽  
Antonio Piccolo ◽  
Gaspare Conio
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Meteorological Data ◽  
Forecasting Model ◽  
Wind Speed Forecasting ◽  
Model Based ◽  
Artificial Neural

24-hours wind speed forecasting and wind power generation in La Serena (Chile)

2018 ◽  
Vol 42 (6) ◽  
pp. 607-623 ◽  
Author(s):  
Ignacio Salfate ◽  
Carlos H López-Caraballo ◽  
Carolina Sabín-Sanjulián ◽  
Juan A Lazzús ◽  
Pedro Vega ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Meteorological Data ◽  
Wind Farms ◽  
Vital Role ◽  
Wind Speed Forecasting ◽  
Wind Potential ◽  
Artificial Neural

This article presents 24-h wind speed forecasting for the city of La Serena in Chile and a methodology to explore forecasting effects on the production of wind turbine power. To that end, we used meteorological data from a weather station located in the southern zone of the hyper-arid Atacama Desert. In this area, energy resources are economically and environmentally important, and wind speed forecasting plays a vital role in the management and marketing processes of wind potential via wind farms. To contribute to the development of this energy, we propose carrying out the short-term prediction of 12 and 24 h ahead (identified as Ws( t + 12) and Ws( t + 24), respectively) using an artificial neural network with backpropagation approach. Hourly time series of wind speed, temperature, and relative humidity (from 2003 to 2006) were considered to characterize the artificial neural network in the training phase, while we used data from the year 2007 to check the efficiency of our prediction. For artificial neural network Ws( t + 12) and Ws( t + 24) models, we obtained similar performance of wind speed prediction with root mean square error of around 0.7 m s−1 and with maximum and minimum residuals of +4 and ‒4 m s−1, respectively. Based on the results, we gain a reliable tool to characterize wind speed properties in the range of 1 day within 20% of uncertainty. Moreover, this tool becomes useful to study the effects of our artificial neural network Ws( t + 12) and Ws( t + 24) models on the generation of wind energy from a wind power turbine parametrization.

scholarly journals Estimation of microclimatic data in remote mountainous areas using an artificial neural network model-based approach

2013 ◽  
Vol 12 (4) ◽  
pp. 384-389
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Data ◽  
Application Site ◽  
Mountainous Area ◽  
Mountainous Areas ◽  
Model Based ◽  
Artificial Neural

An artificial neural network (ANN) model-based approach was developed and applied to estimate values of air temperature and relative humidity in remote mountainous areas. The application site was the mountainous area of the Samaria National Forest canyon (Greece). Seven meteorological stations were established in the area and ANNs were developed to predict air temperature and relative humidity for the five most remote stations of the area using data only from two stations located in the two more easily accessed sites. Measured and model-estimated data were compared in terms of the determination coefficient (R2), the mean absolute error (MAE) and residuals normality. Results showed that R2 values range from 0.7 to 0.9 for air temperature and from 0.7 to 0.8 for relative humidity whereas MAE values range from 0.9 to 1.8 oC and 5 to 9%, for air temperature and relative humidity, respectively. In conclusion, the study demonstrated that ANNs, when adequately trained, could have a high applicability in estimating meteorological data values in remote mountainous areas with sparse network of meteorological stations, based on a series of relatively limited number of data values from nearby and easily accessed meteorological stations.

scholarly journals A Combined Model Based on Society Cognitive Optimization Algorithm for Wind Speed Forecasting

2021 ◽  
Author(s):  
Zhaoshuang He ◽  
Yanhua Chen ◽  
Min Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Optimization Algorithm ◽  
Wind Farms ◽  
Combined Model ◽  
Wind Speed Forecasting ◽  
Forecasting Performance ◽  
Artificial Neural ◽  
Cognitive Optimization

Abstract Wind energy, as renewable energy, has drawn the attention of society. The use of wind power generation can reduce the pollution to the environment and solve the problem of power shortage in offshore islands, grassland, pastoral areas, mountain areas, and highlands. Wind speed forecasting plays a significant role in wind farms. It can improve economic and social benefits and make an operation schedule for wind turbines in large wind farms. At present, researchers have proposed a variety of methods for wind speed forecasting; artificial neural network (ANN) is one of the most commonly used methods. This paper proposes a combined model based on the existing artificial neural network algorithms for wind speed forecasting at different heights. We first use the wavelet threshold method to the original wind speed data set for noise reduction. After that, the three artificial neural networks, extreme learning machine (ELM), Elman neural network, and Long Short-Term Memory neural network (LSTM), are applied for wind speed forecasting. In addition, variance reciprocal method and society cognitive optimization algorithm (SCO) are used to optimize the weight coefficients of the combined model. In order to evaluate the forecasting performance of the combined model, we select wind speed data at three heights (20m, 50m, and 80m) in National Wind Technology Center M2 Tower. The experimental results show that the forecasting performance of the combined model is better than the single model, and it has a good forecasting performance for the wind speed at different heights.

scholarly journals Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Energies ◽  
2017 ◽  
Vol 10 (11) ◽  
pp. 1744 ◽  
Author(s):  
Athraa Ali Kadhem ◽  
Noor Wahab ◽  
Ishak Aris ◽  
Jasronita Jasni ◽  
Ahmed Abdalla
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction ◽  
Artificial Neural
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