A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems

Ekici, Betül Bektaş

doi:10.1016/j.measurement.2014.01.010

Cited by 98 publications

(27 citation statements)

References 50 publications

(55 reference statements)

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“…The solution of the constrained optimization problem of LSSVM in Equation with Lagrange multipliers gives W values such that:

W = true \sum_{k = 1}^{N} α_{k}^{*} ϕ ((), x_{k})

where

α_{k}^{*}

is the Lagrange multiplier, which is calculated by maximization of Equation (Ekici, ). The LSSVM function for estimation can be written as follows:

{\overset{true^}{normaly}}_{k} = normalf ((), x_{k}) = true \sum_{k = 1}^{N} α_{k}^{*} normalK true(x_{k}, x_{m} true) prefix+ b^{*}

where K ( x k , x m ) = ϕ ( x k ) T ϕ ( x m ) for k , m = 1, …, N is the kernel function and b * is the bias term.…”

Section: Methodsmentioning

confidence: 99%

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Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

Okkan

İnan

2014

Intl Journal of Climatology

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ABSTRACT:In this study, statistical downscaling of general circulation model (GCM) simulations to monthly inflows of Kemer Dam in Turkey under A1B, A2, and B1 emission scenarios has been performed using machine learning methods, multi-model ensemble and bias correction approaches. Principal component analysis (PCA) has been used to reduce the dimension of potential predictors of National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. Then, the reasonable GCMs were selected by investigating the rank correlations between the selected predictors in NCEP/NCAR reanalysis data and those in GCMs for 20C3M scenario between periods 1979 and 1999. Upon the training of feedforward neural network (FFNN), least squares support vector machine (LSSVM) and relevance vector machine (RVM) downscaling models, the general performance of the downscaled predictions using NCEP/NCAR reanalysis data for Kemer watershed showed that the trained RVM model produced adequate results. The effectiveness of RVM model was illustrated by its integration with 20C3M scenario between periods 1979 and 1999 and A1B, A2, and B1 future climate scenarios between periods 2010 and 2039. Afterwards, the flow forecasts were obtained by building a multi-model ensemble through the selected GCMs followed by a bias correction approach. Finally, the significance of the probable changes in trends was identified through statistical tests based on the corrected forecasts. Results showed that decreasing flows trends in winter, spring and fall seasons have been foreseen over the study area for the period between 2010 and 2039.

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“…The solution of the constrained optimization problem of LSSVM in Equation with Lagrange multipliers gives W values such that:

W = true \sum_{k = 1}^{N} α_{k}^{*} ϕ ((), x_{k})

where

α_{k}^{*}

is the Lagrange multiplier, which is calculated by maximization of Equation (Ekici, ). The LSSVM function for estimation can be written as follows:

{\overset{true^}{normaly}}_{k} = normalf ((), x_{k}) = true \sum_{k = 1}^{N} α_{k}^{*} normalK true(x_{k}, x_{m} true) prefix+ b^{*}

where K ( x k , x m ) = ϕ ( x k ) T ϕ ( x m ) for k , m = 1, …, N is the kernel function and b * is the bias term.…”

Section: Methodsmentioning

confidence: 99%

“…where * k is the Lagrange multiplier, which is calculated by maximization of Equation (2) (Ekici, 2014). The LSSVM function for estimation can be written as follows:ŷ…”

mentioning

confidence: 99%

Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

Okkan

İnan

2014

Intl Journal of Climatology

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“…With the assumption of linear regression between independent and dependent LSSVM variables, Equation (1) can be re-written as [14] [16]. ( ) ( )…”

Section: Databasementioning

confidence: 99%

“…Radial basis function (RBF) is the utmost used relation for calculating the Kernel function [14] [17].…”

Section: Databasementioning

confidence: 99%

New Methods to Calculate Water Saturation in Shale and Tight Gas Reservoirs

Kamari¹,

Sheng²

2018

OJOGas

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The determination of water saturation is a key step for the reservoir characterization and prediction of future reservoir performance in terms of production. The importance of water saturation has been further identified when the reservoirs refer to rocks with low porosity and permeability such as shale and tight formations. In this communication, two advanced artificial intelligence strategies consisting of least square support vector machine (LSSVM) and gene expression programming (GEP) have been applied in order to develop reliable predictive models for the calculation of water saturation of shale and tight reservoirs. To this end, an extensive core and log data bank has been analysed from 12 wells of a Mesaverde group tight reservoir located in the largest Western US. The results indicate that the estimated water saturation data by the models developed in this study are in satisfactory agreement with the actual log data. Furthermore, new methods proposed in this study are useful for the characterization of shale and tight reservoirs and can be applied to the relevant software.

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“…With the development of big data mining, machine learning technology has attracted widespread attention. For instance, artificial neural network (ANN) [12][13][14][15][16][17] and support vector machine (SVM) [18][19][20] have been widely applied in solar radiation prediction. Amrouche and Le Pivert (2014) [12] took advantage of spatial modeling and artificial neural networks (ANNs) to predict daily total solar radiation in four locations in the United States, and the empirical results indicate that the proposed model satisfies the expected accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

Zhang

Wei

2019

Sustainability

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Precise solar radiation forecasting is of great importance for solar energy utilization and its integration into the grid, but because of the daily solar radiation’s intrinsic non-stationary and nonlinearity, which is influenced by a lot of elements, single predicting models may have difficulty obtaining results with high accuracy. Therefore, this paper innovatively puts forward an original hybrid model that predicts solar radiation through extreme learning machine (ELM) optimized by the bat algorithm (BA) based on wavelet transform (WT) and principal component analysis (PCA). First, choose the meteorological variables on the basis of Pearson coefficient test, and WT will decompose historical solar radiation into two time series, which are de-noised signal and noise signal. In the approximate series, the lag phase of historical radiation is obtained by partial autocorrelation function (PACF). After that, use PCA to reduce the dimensions of the influencing factors, including meteorological variables and historical radiation. Finally, ELM is established to predict daily solar radiation, whose input weight and deviation thresholds gained optimization by BA, thus it is called BA-ELM henceforth. In view of the four distinct solar radiation series obtained by NASA, the empirical simulation explained the hybrid model’s validity and effectiveness compared to other primary methods.

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A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems

Cited by 98 publications

References 50 publications

Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

New Methods to Calculate Water Saturation in Shale and Tight Gas Reservoirs

A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

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