基于LSTM循环神经网络的短期电力负荷预测
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TM910.6

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国家电网有限公司科技项目(4000-201940193A-0-0-00)


Short-term power load forecasting based on LSTM recurrent neural network
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    摘要:

    为了保障电网安全稳定和电力市场高效运行,电网调度人员和电力市场参与者对电力负荷预测准确度提出了更高要求,分布式电源和间歇性负荷是影响负荷精准预测的关键因素。针对传统负荷预测方法无法同时对负荷本身变化规律及其影响因素进行建模的问题,提出基于长短期记忆单元(LSTM)的负荷预测方法。利用具备时序记忆功能的LSTM构建深度循环神经网络(RNN),综合考虑历史负荷和各类负荷影响因素建立负荷预测模型。该方法利用神经网络的特征提取能力和LSTM的时序记忆能力,能在更长的历史时间范围内辨识负荷内在变化规律及各类影响因素对负荷的非线性影响。基于实际负荷数据对不同历史时间窗口、不同网络架构的负荷预测性能进行验证,并与其他负荷预测算法进行比较,结果表明所提方法能有效提升负荷预测准确性。

    Abstract:

    In order to ensure the safety and stability of the power grid and the efficient operation of the power market, grid dispatchers and power market participants have put forward higher requirements for the accuracy of power load forecasting. However, the distributed power and intermittent loads increase the difficulty of predicting loads accurately. In order to solve the problem that the current load forecasting method cannot simultaneously model the change law of the load itself and its influencing factors, load forecasting method based on long short-term memory(LSTM) is proposed. LSTM is used to construct recurrent neural network(RNN), and comprehensively historical load and various load influence factors are considered to establish load forecasting model. The method utilizes the feature extraction ability of the neural network and the memory ability of the LSTM to identify the internal variation law of the load and the nonlinear influence of various influencing factors on the load in a longer historical time range. The actual load datas are used to verify the prediction performance of different historical time windows and different network architectures. Meanwhile, compare with other load prediction algorithms. Experimental results show that the model can improve the accuracy of load forecasting.

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庞传军,张波,余建明.基于LSTM循环神经网络的短期电力负荷预测[J].电力工程技术,2021,40(1):175-180,194

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历史
  • 收稿日期:2020-07-09
  • 最后修改日期:2020-08-27
  • 录用日期:2020-05-12
  • 在线发布日期: 2021-02-03
  • 出版日期: 2021-01-28
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