| 采用BP&SA混合学习策略的短期电力负荷预测方法 |
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| 引用本文： | 丁明,刘盛松.采用BP&SA混合学习策略的短期电力负荷预测方法[J].合肥工业大学学报(自然科学版),2000,23(1):78-80,90. |
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| 作者姓名： | 丁明 刘盛松 |
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| 作者单位： | 合肥工业大学,电气工程学院,安徽,合肥,230009 |
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| 基金项目： | 国家攀登计划，安徽省自然科学基金，930211010，97413001，， |
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| 摘 要： | 提出了一种BP混合模拟退火(SA)的ANN短期负荷预测方法,该方法针对传统BP学习算法的缺点,将BP算法和模拟退火算法的优点相结合以提高网络的学习性能.ANN模型中考虑了温度和预测日类型,可进行工作日和节假日的预测,实例表明ANN模型实用有效、精度高.
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| 关 键 词： | 电力系统短期负荷预测 人工神经网络 模拟退火算法 |
| 修稿时间： | 1999-09-10 |
Short term load forecasting using a neural network with BP & SA hybrid learning algorithm |
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| DING Ming,LIU Sheng-song.Short term load forecasting using a neural network with BP & SA hybrid learning algorithm[J].Journal of Hefei University of Technology(Natural Science),2000,23(1):78-80,90. |
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| Authors: | DING Ming LIU Sheng-song |
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| Abstract: | This
paper proposes a neural network (ANN)with the hybrid learning strategy which combines the
back-propagation(BP) with simulated annealing (SA) algorithm. As back-propagation learning
algorithm has some drawbacks, BP & SA hybrid learning algorithm, which combines the
property of BP with the property of SA algorithm, is adopted to improve the learning
property. The effects of temperature and day of the week are considered in ANN model.
Loads of weekdays and holidays can be forecasted based on the ANN model. Numerical tests
showed the high efficiency and accuracy of the ANN model. |
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