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电力大数据:2023,26(4):-
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基于进化神经网络的短期电网负荷预测算法
刘喜生
(汕头华电发电有限公司)
Short-term Power Grid Load Forecasting Algorithm Based on Evolutionary Neural Network
Liu Xisheng
(SHANTOU HUADIAN POWER CO., LTD.)
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投稿时间:2023-04-05    修订日期:2023-06-15
中文摘要: 本文提出了一种基于进化神经网络的短期电网负荷预测算法。该算法使用改进的人工蜂群算法与BP神经网络融合生成进化神经网络,然后使用改进的人工蜂群算法对进化神经网络的偏置和权重进行优化。该算法将火电历史负荷数据作为输入,使用进化神经网络训练预测模型,预测未来一段时间内的电网负荷。首先,获取历史负荷数据。然后,将获取到的数据输入到进化神经网络模型中进行训练。在训练过程中,采用了改进的人工蜂群算法对进化神经网络对神经网络的权重和偏置进行优化,提高模型的预测精度。人工蜂群算法作为一种全局搜索算法,可以有效地探索模型参数空间,找到最优的模型参数组合,从而提高模型的预测精度。为了验证所提出的负荷预测方法的有效性,我们使用了火电网负荷数据进行了测试。实验结果表明本文提出的进化神经网络在短期电网负荷预测方面表现出了良好的预测精度和实用性。与传统的预测方法相比,该算法的预测误差更小,预测结果更加准确可靠。
Abstract:In this paper, a short-term power network load forecasting algorithm based on evolutionary neural network is proposed. In this paper, an improved artificial bee colony algorithm is combined with BP neural network to generate an evolutionary neural network, and then the bias and weight of the evolutionary neural network are optimized by using the improved artificial bee colony algorithm. The algorithm takes the historical load data of thermal power as input, and uses evolutionary neural network to train the forecasting model to predict the power grid load in the future. First, historical load data is obtained. Then, the obtained data are input into the evolutionary neural network model for training. In the training process, the improved artificial bee colony algorithm is used to optimize the weight and bias of evolutionary neural network to improve the prediction accuracy of the model. As a global search algorithm, artificial bee colony algorithm can effectively explore the model parameter space and find the optimal model parameter combination, thus improving the prediction accuracy of the model. In order to verify the validity of the proposed load forecasting method, we use the load data of thermal power network to test. The experimental results show that the evolutionary neural network proposed in this paper shows good forecasting accuracy and practicability in short-term power grid load forecasting. Compared with traditional prediction methods, the prediction error of this algorithm is smaller and the prediction result is more accurate and reliable.
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