Hourly wind speed forecasting based on Support Vector Machine and Artificial Neural Networks

Wind speed is the main component of wind power. Therefore, wind speed forecasting is of big importance due to its uses. It permits to plan the dispatch, determine the hours of storage needed, the amount of energy stored that should be used and avoid the big fluctuations in the electrical grid caused by the nature of the renewable energy resources. In this paper, we propose four hybrid models based on Support Vector Machine(SVM) and Artificial Neural Networks(ANNs) or just Neural Networks (NN) for wind speed forecasting. Using the Ordinary Least Squares(OLS) analysis for selecting the parameters more influencing wind speed. Then, a Support Vector Machine and Artificial Neural Networks models are tuned by Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). The performance of these models is evaluated using three statistical indicators: the Mean Square Error(MSE), Mean Error(ME) and Mean Absolute Error(MAE). The results show a better performance of the neural model compared to the support vector machine.

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An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.404 ◽

2017 ◽

Vol 7 (9) ◽

pp. 13

Author(s):

Bhargavi Munnaluri ◽

K. Ganesh Reddy

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Speed ◽

Fossil Fuels ◽

Renewable Energy Sources ◽

Support Vector ◽

Hybrid Forecasting ◽

Artificial Neural ◽

Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed. In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

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Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

SPE Drilling & Completion ◽

10.2118/163062-pa ◽

2012 ◽

Vol 27 (04) ◽

pp. 586-595 ◽

Cited By ~ 18

Author(s):

Reza Jahanbakhshi ◽

Reza Keshavarzi ◽

Mahdi Aliyari Shoorehdeli ◽

Abolqasem Emamzadeh

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Oil Fields ◽

Support Vector ◽

Artificial Neural ◽

Offshore Oil

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IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

Radiation Protection Dosimetry ◽

10.1093/rpd/ncy185 ◽

2018 ◽

Vol 184 (1) ◽

pp. 36-43 ◽

Cited By ~ 3

Author(s):

Gal Amit ◽

Hanan Datz

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Support Vector ◽

Physical Parameters ◽

Machine Method ◽

Algorithm Performance ◽

Artificial Neural ◽

First Time ◽

Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

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Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

Advanced Methodologies and Technologies in Modern Education Delivery - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-7365-4.ch059 ◽

2019 ◽

pp. 751-766

Author(s):

Sajid Umair ◽

Muhammad Majid Sharif

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Student Performance ◽

Semantic Analysis ◽

Vital Role ◽

Support Vector ◽

Data Set ◽

Important Research Topic ◽

Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

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