Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts

Authors:
Qi Wu
Affiliations:
Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China and Key Laboratory of Measurement and Control of Complex Systems ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In view of the dissatisfactory capability of the @e-insensitive loss function in field of white (Gaussian) noise of multi-dimensional load series, a new wavelet v-support vector machine with Gaussian loss function which is called Wg-SVM is put forward to penalize the Gaussian noises. To seek the optimal parameters of Wg-SVM, modified genetic algorithm (GA) is proposed to optimize parameters of Wg-SVM. The results of application in load forecasts show that the forecasting approach based on the Wg-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other SVM methods.