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• W2022166137 abstract "Load forecasting is very essential for the efficient and reliable operation of a power system. Often uncertainties significantly decrease the prediction accuracy of load forecasting; this in turn affects the operation cost dramatically as well as the optimal day-to-day operation of the power system. In this article, an overview of recently published work on hybrid neural network techniques to forecast the electrical load demand. A hybrid neural network forecasting model is proposed, which is a combination of simulated annealing (SA) and particle swarm optimization (PSO) called SAPSO. In proposed techniqiue, particle swarm optimization (PSO) algorithm has the ability of global optimization and the simulated annealing (SA) algorithm has a strong searching capability. Therefore, the learning algorithm of a typical three layer feed forward neural network back propagation (BP) is replaced by SAPSO algorithm. Furthermore, preprocessing of input data, convergence, local minima and working of neural network with SAPSO algorithm also discussed." @default.
• W2022166137 created "2016-06-24" @default.
• W2022166137 creator A5073983570 @default.
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• W2022166137 date "2012-12-01" @default.
• W2022166137 modified "2023-10-16" @default.
• W2022166137 title "A review on short term load forecasting using hybrid neural network techniques" @default.
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• W2022166137 doi "https://doi.org/10.1109/pecon.2012.6450336" @default.
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