A new strategy for predicting short-term wind speed using soft computing models

Haque, Ashraf Ul; Mandal, Paras; Kaye, Mary E.; Meng, Julian; Chang, Liuchen; Senjyu, Tomonobu

doi:10.1016/j.rser.2012.05.042

Cited by 82 publications

(36 citation statements)

References 37 publications

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“…ANNs have been used in different fields of science and technology including prediction of different environmental parameters like solar radiation [34,35], wind speed etc. and power forecasting [36,37]. ANN extracts where n is the total number of input and output pairs (which can be vector quantities) used for training, WS iðmeasuredÞ is measured wind speed for i day and WS iðANNÞ is predicted wind speed for i day.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Wind speed prediction in the mountainous region of India using an artificial neural network model

2015

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a b s t r a c tMeasured wind speed data are not available for most sites in the mountainous regions of India. The objective of present study is to predict wind speeds for 11 locations in the Western Himalayan Indian state of Himachal Pradesh to identify possible wind energy applications. An artificial neural network (ANN) model is used to predict wind speeds using measured wind data of Hamirpur location for training and testing. Temperature, air pressure, solar radiation and altitude are taken as inputs for the ANN model to predict daily mean wind speeds. Mean absolute percentage error (MAPE) and correlation coefficient between the predicted and measured wind speeds are found to be 4.55% and 0.98 respectively. Predicted wind speeds are found to range from 1.27 to 3.78 m/s for Bilaspur, Chamba, Kangra, Kinnaur, Kullu, Keylong, Mandi, Shimla, Sirmaur, Solan and Una locations. A micro-wind turbine is used to assess the wind power generated at these locations which is found to vary from 773.61 W to 5329.76 W which is suitable for small lighting applications. Model is validated by predicting wind speeds for Gurgaon city for which measured data are available with MAPE 6.489% and correlation coefficient 0.99 showing high prediction accuracy of the developed ANN Model.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Wind speed prediction in the mountainous region of India using an artificial neural network model

2015

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“…A demonstration of the use of ANFIS can be found in [9,11,29], and Figure 7 depicts the ANFIS structure. …”

Section: Input Layer Hidden Layermentioning

confidence: 99%

“…By way of example, the following references are available, which apply ANN-based models [7][8][9][10][11][12][13][14]; hybrid methods can be found in [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

et al. 2013

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This research presents a comparative analysis of wind speed forecasting methods applied to perform 1 h-ahead forecasting. The main significant development has been the introduction of low-quality measurements as exogenous information to improve these predictions. Eight prediction models have been assessed; three of these models [persistence, autoregressive integrated moving average (ARIMA) and multiple linear regression] are used as references, and the remaining five, based on neural networks, are evaluated on the basis of two procedures. Firstly, four quality indices are assessed (the Pearson's correlation coefficient, the index of agreement, the mean absolute error and the mean squared error). Secondly, an analysis of variance test and multiple comparison procedure are conducted. The findings indicate that a backpropagation network with five neurons in the hidden layer is the best model obtained with respect to the reference models. The pair of improvements (mean absolute-mean squared error) obtained are 29.10%-56.54%, 28.15%-53.99% and 4.93%-14.38%, for the persistence, ARIMA and multiple linear regression models, respectively. The experimental results reported in this paper show that traditional agricultural measurements enhance the predictions.

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“…In [3] the ANFIS have have applied for wind power prediction. Also, the authors in [4] predicted the wind speed using soft computing models formulated on a back propagation neural network (BPNN) and an adaptive neurofuzzy inference system (ANFIS). The adaptive neurofuzzy inference system (ANFIS) has been applied to estimate optimal power coefficient value of the wind turbines by [5].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the parameters of Sugeno based adaptive neuro fuzzy using artificial bee colony: A Case study on predicting the wind speed

Ismail¹,

Aziz²,

Hassanien³

2016

Annals of Computer Science and Information Systems

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Abstract-This paper presents an approach based on Artificial Bee Colony (ABC) to optimize the parameters of membership functions of Sugeno based Adaptive Neuro-Fuzzy Inference System (ANFIS). The optimization is achieved by Artificial Bee Colony (ABC) for the sake of achieving minimum Root Mean Square Error of ANFIS structure. The proposed ANFIS-ABC model is used to build a system for predicting the wind speed. To ensure the accuracy of the model, a different number of membership functions has been used. The experimental results indicates that the best accuracy achieved is 98% with ten membership functions and least value of RMSE which is 0.39.

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A new strategy for predicting short-term wind speed using soft computing models

Cited by 82 publications

References 37 publications

Wind speed prediction in the mountainous region of India using an artificial neural network model

Wind speed prediction in the mountainous region of India using an artificial neural network model

Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

Optimizing the parameters of Sugeno based adaptive neuro fuzzy using artificial bee colony: A Case study on predicting the wind speed

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