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In this paper, a regression technique as the support vector machines (SVM) configured using an optimization technique as the Chu Beasley Genetic Algorithm (CBGA) is proposed to develop a fault location method. As result, a strategy is proposed to relate a set of descriptors obtained from single end measurements of voltage and current (input), to the fault location (output), in a classical regression...
Seawater flue gas Desulfurization (SFGD) was adopted in many coal-fired power plants of littoral for its low cost and high desulfurization efficiency. Operating Parameters would seriously affect SFGD efficiency, the desulfurization efficiency can be improved by adjusting reasonable parameters. this paper applied Least Square Support Machine (LSSVM) to build the studying model of seawater desulfurization...
With the development of thermal power industry, statistics on the NOx emissions become important. In this paper, based on the traditional support vector machine model, we establish support vector machine model optimized by genetic algorithm, improve the prediction accuracy of SVM model. Use the NOx emissions data from 1995 to 2009, predict the NOx emissions from thermal power plant in the year of...
Fly ash unburned carbon content is an important factor affect the boiler thermal's efficiency. Least squares support vector machine is more suitable for real-boiler test conditions with fewer small sample study,We introduced this method into power plant boiler fly ash carbon content prediction model, established complex models relationship between the boiler fly ash carbon content characteristics...
Based on SVM (Support Vector Machine) theory, and the model to predict air conditioning load was established. In order to optimize the behavior of SVM, the DE (Differential Evolution) algorithm was introduced into classic SVM. The DE-SVM model is applied to a real example. The comparisons between the predicted results of the three models-GA (Genetic Algorithm) model, ACO (Ant Colony Optimization)...
Development of competitive power markets has emerged an increasing tendency to forecast the future prices among both the producers and the consumers with the major aim of profit maximization. A least-square support vector machines approach, in combination with a hybrid genetic algorithm based optimization is proposed in this paper to forecast market clearing prices in two different power markets....
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