South China Load Forecasting based on BFO
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| Title | South China Load Forecasting based on BFO |
| Authors | |
| Abstract | Short-term forecasting of electric power load is crucial to electric investment, which is the guarantee of the healthy development of electric industry. The artificial neural network (ANN) was employed in this paper for forecasting. However, ANN is easy to be trapped in local minima, and its convergence speed is too slow. The traditional solving method is to train the ANN via gradient searching techniques, nevertheless, the gradient searching is ineffective. Therefore, bacterial foraging optimization (BFO) was adopted to train the ANN. Besides, leave-one-out cross-validation is chosen for the sake of avoiding over-training. Experiments on the 10/2010-10/2011 historical load data of South China indicate that the proposed BFO-NN is superior to GA-NN, SA-NN, PSO-NN, and ABC-NN, when median square error is considered as the evaluation indicator. |
| Publisher | World Science Publisher |
| Date | 2012-04-04 |
| Source | 2167-6372 |
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