An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case study

Yousefi, Moslem; Hooshyar, Danial; Yousefi, Milad; Khaksar, Weria; Sahari, Khairul Salleh Mohamed; Alnaimi, Firas Basim Ismail

doi:10.1109/icsitech.2015.7407784

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…MSE is an error function for evaluating the performance and efficiency of the forecasting methods. MSE can compare point-by-point for overall performance measure method of the actual time series values and the forecast value [28]. If the values of RMSE and MSE are lower, the accuracy of the model is better.…”

Section: F Prediction Accuracymentioning

confidence: 99%

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Sugiartawan¹,

Pulungan²,

Sari³

2017

ijacsa

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Abstract-Data originating from some specific fields, for instance tourist arrivals, may exhibit a high degree of fluctuations as well as non-linear characteristics due to time varying behaviors. This paper proposes a new hybrid method to perform prediction for such data. The proposed hybrid model of wavelet transform and long-short-term memory (LSTM) recurrent neural network (RNN) is able to capture non-linear attributes in tourist arrival time series. Firstly, data is decomposed into constitutive series through wavelet transform. The decomposition is expressed as a function of a combination of wavelet coefficients, which have different levels of resolution. Then, LSTM neural network is used to train and simulate the value at each level to find the bias vectors and weighting coefficients for the prediction value. A sliding windows model is employed to capture the time series nature of the data. An evaluation is conducted to compare the proposed model with other RNN algorithms, i.e., Elman RNN and Jordan RNN, as well as the combination of wavelet transform with each of them. The result shows that the proposed model has better performance in terms of training time than the original LSTM RNN, while the accuracy is better than the hybrid of wavelet-Elman and the hybrid of wavelet-Jordan.

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Section: F Prediction Accuracymentioning

confidence: 99%

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Sugiartawan¹,

Pulungan²,

Sari³

2017

ijacsa

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“…For the sake of comparison, a back propagation feed-forward neural network similar to the work of [3] is employed for constructing the artificial neural network and forecasting the 1-hour ahead wind speed data. Among supervised structures, ANN is the most commonly used one and has been successfully adopted for both short-and long-term forecasting of time series where normally a defined error function, which is typically mean square error, is minimized using a gradient descent method [12].…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

“…In this section, a simple weighted hybrid of these two models is developed and tested on the checking data. The following weighted model is used in (3).…”

Section: Anfis-ann Hybrid Attemptmentioning

confidence: 99%

“…Recently, the wavelet transform (WT) has been used for this task and for eliminating, or reducing to the least, of this noise. Yousefi et al [3] showed in a recent study that pretreatment of wind speed data series using wavelet transform and could improve the efficiency of a neural network in predicating the wind speed in short-term horizons) [4][5][6]. For building a prediction model, the redundant highly auto-correlated wind speed data could be eliminated to improve the prediction accuracy as it has been previously shown in the literature that wind speed data has a high degree of autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

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Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Yousefi

Hooshyar

Remezani

et al. 2015

Int. J. Adv. Intell. Informatics

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Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurate

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“…Applications have been increased after neural networks were able to solve indissoluble problems in recent years. For instance, Yousefi et al (2015) used ANN to model the nonlinearity of wind speed to accurately forecast wind speed in wind farms. Markopoulos et al (2016) compared the performances of various ANNs in predicting surface roughness.…”

Section: Neural Networkmentioning

confidence: 99%

Modeling and forecasting US presidential election using learning algorithms

Zolghadr

Niaki

2017

J Ind Eng Int

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The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president's approval rate, and others are considered in a stepwise regression to identify significant variables. The president's approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method's calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.

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An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case study

Cited by 16 publications

References 9 publications

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Modeling and forecasting US presidential election using learning algorithms

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