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基于改进LSTM算法的综合能源系统多元负荷预测

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准确预测短期多种能源负荷,是确保综合能源系统可靠、高效运行的必要前提.为此,提出了一种基于遗传粒子群混合优化(genetic algorithm particle swarm optimization,GAPSO)算法的卷积长短期记忆神经网络(convolutional neural network-long short-term memory,CNN-LSTM)综合能源系统多元负荷预测模型.首先,利用皮尔逊系数来描述各影响因素与负荷之间的相关性强弱.其次,采用GAPSO算法对长短期记忆(long short-term memory,LSTM)网络模型进行改进,然后构建卷积神经网络(convolutional neural networks,CNN)以提取小时级高阶特征,并通过改进后的LSTM网络模型对提取的隐含高阶特征进行分位数回归建模,构建了基于GAPSO-CNN-LSTM综合能源系统多元负荷预测模型.最后,以美国亚利桑那州立大学坦佩校区综合能源系统负荷数据为算例进行验证,结果表明:改进后的算法具有更好的收敛能力,模型具有更高的预测精度.
Multiple Load Forecasting of Integrated Energy System Based on Improved LSTM Algorithm
Accurate prediction of short-term multiple energy loads is a prerequisite to ensure the reliable and efficient operation of integrated energy system. For this reason, a convolutional neural network-long short-term memory (CNN-LSTM) model for integrated energy system multivariate load prediction based on genetic algorithm particle swarm optimization (GAPSO) is proposed. Firstly, Pearson's coefficient is used to describe the correlation between the influencing factors and the load. Secondly, GAPSO algorithm is used to improve the LSTM model, and then a one-dimensional CNN is constructed to extract the hourly higher-order features, and the extracted implicit higher-order features are partitioned by the improved long short-term memory (LSTM) modeling. The multivariate load forecasting model based on GAPSO-CNN-LSTM for integrated energy system is constructed through quantile regression modeling. Finally, the load data of integrated energy system of Arizona State University Tempe Campus is used as an example, and the results show that the improved algorithm has a better convergence ability and the model has a higher prediction accuracy.

long short-term memory (LSTM)convolutional neural networks (CNN)genetic algorithm particle swarm optimization (GAPSO)integrated energy systemsload forecasting

闫照康、马刚、冯瑞、徐健玮、沈静文

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南京师范大学电气与自动化工程学院,江苏省 南京市 210042

长短期记忆(LSTM) 卷积神经网络(CNN) 遗传粒子群混合优化(GAPSO)算法 综合能源系统 负荷预测

江苏省重点研发计划(产业前瞻与关键核心技术)

BE2020081

2024

10.16513/j.2096-2185.DE.2409204

分布式能源
中国大唐集团科学技术研究院有限公司,清华大学出版社有限公司

分布式能源

CSTPCD
ISSN：2096-2185
年,卷(期)：2024.9(2)
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