区域综合能源系统短期负荷预测

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Network World Pub Date : 2021-01-01 DOI:10.14311/nnw.2021.31.023
Jianyu Wang
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引用次数: 0

摘要

在对Elman网络进行理论分析的基础上,建立了区域综合能源系统短期负荷预测模型。通过反复的离线训练和实验,确定了模型的结构和参数。预测精度显著高于传统BP网络,预测误差小于3%,能够满足区域综合能源系统协调调度的需要。
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Short-term load forecasting of regional integrated energy system
Based on the theoretical analysis of Elman network, the short-term load forecasting model of regional integrated energy system is established. The structure and parameters of the model are determined through repeated off-line training and experiments. The forecasting accuracy is significantly higher than that of traditional BP network, and the prediction error is less than 3%, which can meet the needs of coordination and scheduling of regional integrated energy system.
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来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
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