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In this paper, the load forecasting model is established to increase the precision of meteorological impacts, temperature, short-term power load forecasting, working and holiday factors by considering the power load. Further, IGA-LS-SVM is proposed which is a short-term power load forecasting technique based on AI algorithm. And to increase the forecast accuracy and generalization capability of LS-SVM, we applied the adopted mutation probability and new coding technology to the parameter optimization of LS-SVM. The temperature, load, weather state, working and holiday days be taken as prediction model as input, and load value was predicted output. We selected the sample data from meteorological information and historical load of a city in Yunnan province. By results, the prediction verifies the good prediction effect when associated with existing BP algorithm and the proposed IGA- LS-SVM algorithm yields a value 0.8274 more significant than all others, which is appropriate for short-term power load prediction. [ABSTRACT FROM AUTHOR]

To achieve robust estimation for noisy data set, a recursive outlier elimination-based least squares support vector machine (ROELS-SVM) algorithm is proposed in this paper. In this algorithm, statistical information from the error variables of least squares support vector machine is recursively learned and a criterion derived from robust linear regression is employed for outlier elimination. Besides, decremental learning technique is implemented in the recursive training–eliminating stage, which ensures that the outliers are eliminated with low computational cost. The proposed algorithm is compared with re-weighted least squares support vector machine on multiple data sets and the results demonstrate the remarkably robust performance of the ROELS-SVM. [ABSTRACT FROM AUTHOR]

Abstract  Aiming at the common support vector machine’s biased disadvantage and computational complexity, an unbiased least squares support vector machine (LSSVM) model is proposed in this paper. The model eliminates the bias item of LSSVM by improving the form of structure risk, then the unbiased least squares support vector classifier and the unbiased least squares support vector regression are deduced. Based on this model, we design a new learning algorithm using Cholesky factorization according to the characteristic of kernel function matrix, in this way the calculation of Lagrangian multipliers is greatly simplified. Several experiments on diffenert datasets are carried out, including the common datasets classification, synthetic aperture radar image automatic target recognition and chaotic time series prediction. The experimental results of correct recognition rate and the fitting precision testify that the unbiased LSSVM model has good universal ability and fitting accuracy, better generalization capability and stability, and have a great improvement in learning speed. [ABSTRACT FROM AUTHOR]