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基于贝叶斯神经网络短期负荷预测模型

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Keywords: 贝叶斯神经网络,短期负荷预测,蒙特卡洛算法,先验概率分布,汉密尔顿动力系统

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Abstract:

?本文提出了基于贝叶斯神经网络(BNN)短期负荷预测模型。根据气象影响因素和电力负荷的样本数据,针对权向量参数的先验分布分别为正态分布和柯西分布两种情况,应用混合蒙特卡洛(HMC)算法学习了BNN的权向量参数。由HMC算法和Laplace算法学习的贝叶斯神经网络以及BP算法学习的传统神经网络分别对4月(春)、8月(夏)、10月(秋)和1月(冬)每月25天的每个整点时刻的负荷进行了预测。这些神经网络的输入层有11个节点,它们分别与每个整点时刻和的气象因素、上一个整点时刻的气象因素和时间变量相对应,输出层只有一个节点,它与负荷变量对应。试验结果表明HMC算法学习的BNN的预测结果的百分比平均绝对误差(MAPE)和平方根平均误差(RSME)取值远远小于由Laplace算法学习的BNN和BP算法学习的人工神经网络的MAPE和RMSE。而且,HMC算法学习的BNN在测试集和训练集上的预测误差MAPE和RMSE的相差很小。实验结果充分说明HMC算法学习的BNN具有较高的预测精度和较强的泛化能力。

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