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SHORT-TERM LOAD FORECASTING USING A MULTILAYER NEURAL NETWORK WITH AN ADAPTIVE LEARNING ALGORITHM
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DOI：10.1109/59.141697.png)
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Abstract
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A multilayer neural network with an adaptive learning algorithm is designed for short term load forecasting. Extensive studies have been performed on the effect of various factors such as learning rate, momentum, the number of presentations in an iteration, etc. on the efficiency and accuracy of the backpropagation-momentum learning method which is employed in the training of artificial neural networks. To speed up the training process, a new learning algorithm for the adaptive training of neural networks is presented. The effectiveness of the neural network with the proposed adaptive learning algorithm is demonstrated by short term load forecasting of Taiwan power system. It is found that, once trained by the proposed learning algorithm, the neural network can yield the desired hourly load forecast very efficiently and accurately. Moreover, the proposed adaptive learning algorithm converges much faster than the conventional backpropagation-momentum learning method.
Keywords:
LOAD FORECASTING
ARTIFICIAL NEURAL NETWORKS
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING SYSTEMS
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