Implementation of GA-LSSVM modelling approach for estimating the performance of solid desiccant wheels

Zendehboudi, Alireza

doi:10.1016/j.enconman.2016.08.070

Cited by 51 publications

(17 citation statements)

References 31 publications

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“…Least squares support vector machine (LSSVM) was introduced in [44] as a reformulation of the standard SVM [45,46] that simplifies the model to a great extent by applying linear least squares criteria to the loss function instead of the traditional quadratic programming method. The simplicity and inherited advantages of SVM, such as its being based on the principle of minimizing structural risk and its kernel mapping, promote the application of LSSVM in many pattern recognition and regression problems [47][48][49][50]. Zheng et al [51] introduced LSSVM to dissolved gas content forecasting and made forecasts for the five gases.…”

Section: Related Workmentioning

confidence: 99%

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

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Section: Related Workmentioning

confidence: 99%

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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“…In order to verify the predictive performance, the proposed BSOA-based LSSVM is compared with GA optimized LSSVM (Zendehboudi, 2016), PSO optimized LSSVM (Gorjaei et al, 2015) and improved feature selection (IFS) optimized LSSVM . The programming of all the algorithms is implemented in MATLAB.…”

Section: Simulationmentioning

confidence: 99%

“…However, these methods have some shortcomings, such as complex computation process and long optimization time. Some other scholars apply some heuristic algorithms to the parameter optimization of LSSVM model, which include genetic algorithm (GA) (Padilha et al, 2016;Zendehboudi, 2016), particle swarm optimization (PSO) (Gorjaei et al, 2015;Long et al, 2014), free search algorithm , ant colony algorithm (Huang and Deng, 2010;Liu and Zhang, 2016), artificial bee colony algorithm (Jain et al, 2016;Sulaiman et al, 2012) and so on. Although these heuristic algorithms have achieved satisfactory results, there are also many problems such as complexity of algorithms, large randomness of optimization results and the influence of initial parameters of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019

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Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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“…. In this study, LSSVM is adopted in that it is expert in addressing small sample problems [21] and has high generalization performance [30]. LSSVM is a type of powerful artificial intelligence technology based on the structural risk minimization.…”

Section: Multikernel Lssvm(mklssvm)mentioning

confidence: 99%

A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization

Sun

Zhu

et al. 2018

Mathematical Problems in Engineering

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The aims of this study contribute to a new hybrid model by combining ensemble empirical mode decomposition (EEMD) with multikernel function least square support vector machine (MKLSSVM) optimized by hybrid gravitation search algorithm (HGSA) for short-term wind speed prediction. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions (IMFs) and one residual firstly. Then, partial autocorrelation function (PACF) is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function (RBF) and polynomial (Poly) kernel function by weight coefficient is exploited as core forecasting engine to make the short-term wind speed prediction. To improve the regression performance, the binary-value GSA (BGSA) in HGSA is utilized as feature selection approach to remove the ineffective candidates and reconstruct the most relevant feature input-matrix for the forecasting engine, while real-value GSA (RGSA) makes the parameter combination optimization of MKLSSVM model. In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Numerical results and comparable analysis illustrate the excellent performance of the EEMD-HGSA-MKLSSVM model when applied in the short-term wind speed forecasting.

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Implementation of GA-LSSVM modelling approach for estimating the performance of solid desiccant wheels

Cited by 51 publications

References 31 publications

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization

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