Short Term Load Forecasting using Generalized Neuron Model with Error Gradient Functions
نویسنده
چکیده
Short Term Load Forecasting(STLF) varies from an hour to hour and is used for requirement for control, unit commitment, security assessment, optimum planning of power generation, and planning of both spinning reserve and energy exchange, also as inputs to load flow studies and contingency analysis. Artificial neural networks (ANN’s) has drawbacks like inputs nodes or hidden nodes which can cause training file difficulties, more computation time, large size data, less flexibility etc. Generalized neuron model (GNM) have more flexibility, no hidden layers, less computation time, usage of and neurons etc. In this paper, development of STLF using GNM under different error gradients functions is obtained.
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