Load forecasting is an important subject for power distribution systems and has been studied from different points of view. This paper aims at the Gaussian noise parts of load series the standard v-support vector regression machine with @e-insensitive loss function that cannot deal with it effectively. The relation between Gaussian noises and loss function is built up. On this basis, a new v-support vector machine (v-SVM) with the Gaussian loss function technique named by g-SVM is proposed. To seek the optimal unknown parameters of g-SVM, a chaotic particle swarm optimization is also proposed. And then, a hybrid-load-forecasting model based on g-SVM and embedded chaotic particle swarm optimization (ECPSO) is put forward. The results of application of load forecasting indicate that the hybrid model is effective and feasible.

A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization

In view of the bad forecasting results of the standard epsiv-support vector machine (SVM) for product sale series with the normal distribution noise, a SVM based on the Gaussian loss function named by g-SVM is proposed. And then, a hybrid forecasting model for product sales and its parameter-choosing algorithm are presented. The results of its application to car sale forecasting indicate that the short-term forecasting method based on g-SVM is effective and feasible.

A Forecasting Model Based Support Vector Machine and Particle Swarm Optimization

In view of the dissatisfactory forecasting capability of standard support vector machine SVM for product sale series with normal distribution noises, a new SVM, called g-SVM, with Gaussian function used as its loss function, is proposed. It is theoretically proved that an adjustable parameter of g-SVM is equal to not only the upper bound of the proportion of erroneous samples to total samples but also the lower bound of the proportion of support vectors to total samples; in other words, the number of erroneous samples is fewer than or equal to that of support vectors. A new version of particle swarm optimization PSO with the integration of Logistic mapping and standard PSO is proposed for an optimal parameter combination of g-SVM. With the above, a short-term intelligent forecasting method based on g-SVM and the proposed PSO is then put forth. The results of its application to car-sales forecasting indicate that the forecasting method is feasible and effective.

A short-term forecasting model with inhibiting normal distribution noise of sale series

Study of Modern Securities Pricing Model

Basing on the biological evolution scheme "random evolution plus feedback", Baldwin effect based chaotic parallel genetic algorithm with variable-scale learning, is proposed by introducing the chaotic mapping and posterior Baldwin learning into the standard genetic algorithm. The chaotic mapping maintains a good diversity of population during evolving; and Baldwin learning with variable-scale speeds up the evolution along the right direction. The performance of new GA is demonstrated on a benchmark problem. Experimental results show that the new algorithm is efficient and robust.

Baldwin Effect Based Chaotic Parallel Genetic Algorithm with Variable-Scale Learning

Lots of improvements have been made to genetic algorithm, but they did not nearly solve the dilemmas - slow convergence and crowding problem due to the conventional genetic algorithms' oversimplified mechanisms: pseudo-diversity of population and randomized evolutionary operation. Basing on a new scheme -random evolution plus feedback, which is reported to well represent the nature of biological evolution process, we propose chaotic parallel genetic algorithm with feedback mechanism. In this new algorithm, chaotic mapping is embedded for maintaining a good diversity of population; and Baldwin effect based posterior reinforcement learning, which can successfully deal with the feedback information from evolutionary system, is included to speed up the evolution along the right direction. The performance of this new algorithm was demonstrated on two well-known benchmark constrained problems. Results show that this new genetic algorithm is feasible and quite effective.

http://ieeexplore.ieee.org/iel5/9872/31387/01460483.pdf

Chaotic parallel genetic algorithm with feedback mechanism and its application in complex constrained problem

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Chaotic parallel genetic algorithm with feedback mechanism and its application in complex constrained problem