Proceedings of the CSEE · 2006 · 36 citations · 0 references
ComputingComputer ScienceData ScienceData MiningForecastingMachine LearningData PreprocessingLoad ForecastingShort-term ForecastingSupport Vector MachinesTime Series ForecastingTime Series PredictionLarge-scale Data ProcessingMachine Learning-based ForecastingNeural ForecastingRedundant ComputationShort-term Load ForecastingSupport Vector ClassificationSupport Vector Machine ClassificationSupport Vector Regression
The support vector machine (SVM) has been successfully applied to the load forecasting area, but it has some disadvantages of very large data amount and slow processing speed. Using advantages of the data mining technology in processing large data and eliminating redundant information, a SVM forecasting system based on data mining preprocess was proposed to search the historical daily load with the same meteorological category as the forecasting day and to compose data sequence with highly similar meteorological features. Taking the new data sequence as the training data of SVM, the data amount was decreased and the processing speed was improved. This approach has achieved greater forecasting accuracy comparing with the method of single SVM and BP neural network.