This paper creates a system for power load forecasting using support vector machine and ant colony optimization. The method of colony optimization is employed to process large amount of data and eliminate redundant information. The system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features. With this method, we reduced SVM training data and overcame the disadvantage of very large data and slow processing speed when constructing SVM model. This paper proposes a new feature selection mechanism based on ant colony optimization in an attempt to combat the aforemention difficulties. The method is then applied to find optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring, the ant colony optimization can mine the data more overall and accurate than the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparing with single SVM and BP neural network in short-term load forecasting, this new method can achieve greater forecasting accuracy. It denotes that the SVM-learning system has advantage when the information preprocessing is based on data mining technology.