Swarm intelligence
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Electric load forecasting using a fuzzy ART&ARTMAP neural network
Applied Soft Computing
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
Hybrid robust support vector machines for regression with outliers
Applied Soft Computing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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This paper proposes a practical new hybrid model for short term electrical load forecasting based on particle swarm optimization (PSO) and support vector machines (SVM). Proposed PSO-SVM model is targeted for forecast load during periods with significant temperature variations. The proposed model detects periods when temperature significantly changes based on weather (temperature) forecast and decides whether the model can be trained just on recent history (typically 4 weeks ago) or such history has to be modified with data for similar days taken from history beyond recent history when such weather conditions were detected. Architecture of the solution consists of three modules, preprocessing module, SVM module and PSO module. The algorithm has been tested in city of Burbank utility, USA and obtained results show better accuracy comparing to results generated with classical methods of training on recent history only or similar days only.