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Power load forecasting is essential in the task scheduling of every electricity production and distribution facility. This paper studies the application of a variety of tuning techniques for optimizing the least squares support vector machines (LS-SVM) hyper-parameters in a short-term load forecasting problem. Clearly, the construction of any effective and accurate LS-SVM model depends on carefully setting the associated hyper-parameters. As a result, available optimization techniques including genetic algorithms (GA), simulated annealing (SA), Bayesian evidence framework and cross validation (CV) are applied and then compared for performance time, accuracy and computational cost. As a measure of effectiveness, the introduced algorithms are trained and tested on historical data obtained from Ontario's Independent Electricity System Operator (IESO) for the Canadian city, Toronto. Experimental results show that optimized LS-SVM by Bayesian framework can achieve greater accuracy and faster speed than other techniques including LS- SVM tuned with genetic algorithm, simulated annealing and 10- fold cross validation.