Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Supplier selection: A hybrid model using DEA, decision tree and neural network
Expert Systems with Applications: An International Journal
Fuzzy-rough data reduction with ant colony optimization
Fuzzy Sets and Systems
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Conventional regression versus artificial neural network in short-term load forecasting
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Power load forecasting using data mining and knowledge discovery technology
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Power load forecasting using data mining and knowledge discovery technology
International Journal of Intelligent Information and Database Systems
Short-term power load forecasting using grey correlation contest modeling
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Chaotic time series prediction with employment of ant colony optimization
Expert Systems with Applications: An International Journal
A framework for short-term activity-aware load forecasting
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
Expert Systems with Applications: An International Journal
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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.