Application of support vector machine models for forecasting solar and wind energy resources: A review

Zendehboudi, Alireza; Baseer, Mohammed Abdul; Saidur, R.

doi:10.1016/j.jclepro.2018.07.164

Cited by 458 publications

(148 citation statements)

References 84 publications

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“…For regression problems, the functions of the kernel are often to be used to predict resulting outcome, such as radial basis function (RBF), two neural networks, polynomial, sigmoid and linear, exponential radial basis function (ERBF) [55,56]. In recent years, SVM has been applied in many fields as well as publications, therefore, the details of the SVM are not presented in this study but can be found in [57][58][59][60][61][62][63].…”

Section: Svmmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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Section: Svmmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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“…The material type largely governs the absorption efficiency of solar energy. At present, the conventional solar collector materials are highly priced, easily corrode and inconvenient to replace, having low solar energy absorption efficiencies in the mid infrared band [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Preparation and properties of endothermic functional ceramics with iron tailings as raw materials

Meng

Peng

Srinivasakannan

et al. 2020

Mater. Res. Express

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The present work attempts to utilize iron tailings as the main raw materials for preparation of functional ceramics as a means of resource utilization. A new type of endothermic functional ceramics, which can be used in the field of solar energy, was prepared via semi-dry pressing followed by pressureless sintering. The results show that the functional ceramics made of 68 wt% iron tailings, 26 wt% iron ore, 3 wt% alumina, 1 wt% potash feldspar and 2 wt% kaolin, sintered at 1185°C exhibited the best overall performance. Visual observation reveals that there were no cracks on the surface of the samples even after 20 cycles of intense thermal shock. Other attributes could be summarized as follows: Infrared emissivity in mid-infrared region: 0.85; thermal conductivity at 500°C: 2.066 W/ (m·K); flexural strength: 119.03 MPa. XRD analysis indicates that the main crystalline phases in the samples are augite, magnetite and a small amount of hematite. An increase in the proportion of iron oxide contributed to lower melting temperature of the functional ceramics, deepen color, promote densification and increase infrared emissivity. In short, the introduction of iron tailings improves the thermal and physical properties to a certain extent.

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“…In recent years, China has faced enormous environmental and energy challenges, which have a significant influence on economic growth, public health, and welfare (Li et al, 2018a). With the decline of conventional energy sources and increasing environmental pollution, the energy structure based on fossil fuels will be replaced by a cleaner energy structure (Zendehboudi et al, 2018). Wind energy has become increasingly popular worldwide, and it can make a great contribution to reduce CO2 emissions (Wang et al, 2008;Wang and Li, 2019).…”

Section: Introductionmentioning

confidence: 99%

Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China

Yang

Wang

et al. 2019

Journal of Cleaner Production

123

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Wind energy, acknowledged as a promising form of renewable energy and the fastestgrowing clean method for electricity generation, has attracted considerable attention from many scientists and researchers in recent decades. However, wind energy forecasting is still a challenging task owing to its inherent features of non-linearity and randomness. Therefore, this study develops a hybrid wind energy forecasting and analysis system including a deterministic forecasting module and an uncertainty analysis module to mitigate the challenges in existing studies. In particular, these challenges are as follows: (1) It is difficult to guarantee that the data characteristics underlying the time series are effectively extracted; (2) in the modeling of each subseries, i.e., when the original data is decomposed into some time series, forecasting accuracy and stability are not simultaneously considered, and thus, they are not properly modeled; and (3) the best function to perform a deterministic forecasting and uncertainty analysis based on the forecaster of each subseries is unknown. The developed hybrid system consists of three steps: First, data preprocessing is conducted to capture and mine the main feature of the wind energy time series and weaken the noises' negative effects; second, multi-objective optimization is proposed to achieve the forecasting of each subseries with improvements in accuracy and stability; finally, search for the best function, which obtains the deterministic forecasting and uncertainty analysis results using an optimized extreme learning machine based on different modeling objectives, is conducted. Experimental simulations are performed using data from three sites in a real wind farm, which indicate that the developed system has a better performance in engineering applications than that of other methods. Furthermore, this system could not only be used as an effective tool for wind energy deterministic forecasting and uncertainty analysis, but also for other engineering application areas in the future.

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Application of support vector machine models for forecasting solar and wind energy resources: A review

Cited by 458 publications

References 84 publications

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Preparation and properties of endothermic functional ceramics with iron tailings as raw materials

Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China

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