Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada

Maqsood, Imran; Khan, Muhammad Riaz; Huang, Guohe; Abdalla, Rifaat

doi:10.1016/j.engappai.2004.08.019

Cited by 62 publications

(19 citation statements)

References 23 publications

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“…Maqsood et al [112] present the idea of using more than one model to forecast three meteorological variables (including wind speed) for a 24-hr-ahead interval. Four different types of NNs [113] are considered: the multilayer perceptron (MLP), the recurrent neural network of Elman, the radial basis function (RBF), and the Hopfield neural networks.…”

Section: Damousis and Dokopoulosmentioning

confidence: 99%

“…[109], [110], [118], [119] Smooth Transition Autoregressive [136]- [138] Discrete Hilbert Transform [120], [121] Markov-switching Autoregressive [136]- [138] Abductive Networks (GMDH) [114] Adaptive Fuzzy Logic Models [122], [123] Adaptive Linear Models [122], [123] ARIMA time series models [94]- [100], [106], [128]- [130] Neural Networks [104]- [108], [112], [131] Adaptive Neural Fuzzy Inference System [106], [116], [127] …”

Section: Synthesis Of the Literature Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

302

264

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Section: Damousis and Dokopoulosmentioning

confidence: 99%

Section: Synthesis Of the Literature Overviewmentioning

confidence: 99%

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

302

264

View full text Add to dashboard Cite

“…Each neuron receives signals from the neurons of the previous layer weighted by the weighted connections between neurons except in the input layer. Neurons then produce an output signal by passing the summed signal through an activation function (Maqsood et al 2005;Ghavidel and Montaseri 2014). The gradient descent, conjugate gradient, Levenberg-Marquardt, and etc.…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

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Salinity is one of the main factors in groundwater quality monitoring. The main objectives of this study are to investigate and compare the accuracy of three different neural computing techniques, multi-layer perceptron neural network (MLP), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN), in prediction of groundwater salinity of the Tabriz plain confined aquifer, expressed by electrical conductivity [EC (lS/cm)], and to employ an integrated method to combine the advantages of neural network models utilizing the concept of committee machine. To develop the models, 93 data records of groundwater samples were collected from East Azarbaijan regional water company. The data set including Ca 2? , Mg 2? , Na ? , SO 4 2-and Cl -concentrations as the inputs and salinity [EC (lS/ cm)] as an output were divided into two subsets; training and testing based on cross validation approach. After training and testing of the models, the performance of the models were evaluated using root mean square errors (RMSE), determination coefficient (R 2 ) and mean absolute error (MAE). The performance criteria of the constructed neural network models showed that RBFNN model has the best performance in predicting salinity. The committee neural network (CNN) combined the results of salinity predicted from MLP, RBFNN and GRNN, each of them has a weight factor showing its contribution in overall prediction. The optimal weights were derived by a genetic algorithm (GA). The results of salinity prediction derived from CNN showed that the CNN performs better than any one of the individual ANNs acting alone for predicting groundwater salinity.

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“…Machine learning-based techniques such as multi-layer perceptron (MLP) neural networks [22], Radial Basis Function (RBF) neural networks [23], recurrent neural networks [24,25], MLP neural networks with correlation analysis as the feature selection technique [10,26], and Adaptive Neuro Fuzzy Inference System (ANFIS) [1,27] have also been proposed to predict wind speed. In Reference [6], a fuzzy model is suggested for the prediction of wind speed and the produced electrical power by means of a training set, which includes wind speed and direction data.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid iterative method for short-term wind speed forecasting

Amjady

Keynia

Zareipour

2010

Euro. Trans. Electr. Power

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SUMMARYForecasting wind power is recognized as a tool in mitigating the operational challenges imposed on power systems by large-scale integration of intermittent wind-powered generators. Wind energy is directly dependent upon wind speed, which is a complex signal to model and forecast. In this paper, a new Hybrid Iterative Forecast Method (HIFM) for wind speed forecasting is presented which takes into account the interactions of temperature and wind speed. To select the most relevant and the less redundant input variables from the available data, a two-stage feature selection technique is also introduced. The forecast accuracy of the proposed wind power prediction strategy is evaluated by means of real data of wind power farms of Iran and Spain's power systems.

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Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada

Cited by 62 publications

References 23 publications

Wind power forecasting : state-of-the-art 2009.

Wind power forecasting : state-of-the-art 2009.

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

A new hybrid iterative method for short-term wind speed forecasting

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