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一种采用记忆神经网络和曲线形状修正的负荷预测方法

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针对分布式电源和新型负荷容量累积造成负荷影响因素多元化和不确定性特性增强的问题,文中提出一种采用记忆神经网络和曲线形状修正的负荷预测方法.在负荷峰值预测中,采用最大信息系数计算负荷峰值与影响因素的非线性相关性,实现对输入特征的筛选;综合考虑负荷峰值序列的长短期自相关性和输入特征与负荷峰值的不同程度相关性,结合Attention机制和双向长短时记忆(bidirectional long short-term memory,BiLSTM)神经网络建立负荷峰值预测模型.在负荷标幺曲线预测中,通过误差倒数法组合相似日和相邻日,建立负荷标幺曲线预测模型;针对预测偏差的非平稳特征,利用自适应噪声的完全集成经验模态分解和BiLSTM网络建立误差预测模型,对曲线形状进行修正.应用中国北方某城市的区域电网负荷数据为算例,验证了所提模型的有效性.
A load prediction method using memory neural network and curve shape correction
Aiming at the problems that multiplex influencing factors and strong uncertainty in distribution network load caused by the capacity accumulation of distributed generation and new loads,a load prediction method using memory neural network and curve shape correction is proposed.In load peak prediction,the maximum information coefficient is applied to calculate the nonlinear correlation between load peak and influencing factors,so as to select the input features.Considering the long-term and short-term autocorrelation in load peak sequence and the different correlation between input features and load peak,the load peak prediction model is established with the Attention mechanism and bidirectional long-short term memory(BiLSTM)neural network.In load per-unit curve prediction,a prediction model is established by combining similar day and adjacent day through the reciprocal error method.In view of the non-stationary characteristics of prediction deviation,the complete ensemble empirical mode decomposition with adaptive noise and BiLSTM network are used to establish an error prediction model to correct the curve shape.The validity of the proposed model is verified by an example of regional power grid load of a city in northern China.

ultra-short term load predictionattention mechanismbidirectional long-short term memory(BiLSTM)neural networkload peakload per-unit curvecurve shape correction

张家安、李凤贤、王铁成、郝妍

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河北工业大学省部共建电工装备可靠性与智能化国家重点实验室,天津 300130

河北工业大学电气工程学院,天津 300130

河北工业大学人工智能与数据科学学院,天津 300401

河北工业大学实验实训中心,天津 300401

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超短期负荷预测 Attention机制 双向长短时记忆(BiLSTM)神经网络 负荷峰值 负荷标幺曲线 曲线形状修正

河北省自然科学基金资助项目

E2020202142

2024

10.12158/j.2096-3203.2024.01.013

电力工程技术
江苏省电力公司 江苏省电机工程学会

电力工程技术

CSTPCD北大核心
影响因子：0.969
ISSN：2096-3203
年,卷(期)：2024.43(1)
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