| 一种电力负荷预测混合模型研究 |
投稿时间：2019-10-13 修订日期：2019-10-26  点此下载全文 |
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| 中文摘要:为了提高短期负荷预测(STLF)的精度问题,本文采用新的信号分解和相关分析技术,结合改进的经验模态分解法(IEMD)将负荷需求时间序列分解为若干个规则的低频分量。为了补偿信号分解过程中的信息损失,通过使用T-Copula进行相关分析来合并外部变量的影响。通过T-Copula分析,可从风险值(VaR)得出峰值负荷指示二进制变量,以提峰值时间负荷预测的准确性。将IEMD和T-Copula得到的数据应用于深度置信网络(DBN)来预测特定时间的未来负荷需求。 |
| 中文关键词:短期负荷预测 经验模态分解 T-Copula 峰值负荷 风险值 深度置信网络 |
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| A Hybrid Model for Power Load Forecasting |
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| Abstract:In order to improve the accuracy of short-term load forecasting (STLF), this paper uses new signal decomposition and correlation analysis technology, combined with improved empirical mode decomposition (iemd) to decompose the load demand time series into several regular low-frequency components. In order to compensate for the information loss during signal decomposition, T-Copula is used for correlation analysis to merge the effects of external variables. Through T-Copula analysis, the binary variable indicating peak load can be obtained from the value of risk (VaR) to improve the accuracy of peak time load forecasting. The data from IEMD and T-Copula are applied to deep confidence network (DBN) to predict future load demand at a specific time. |
| keywords:short-term load forecasting empirical mode decomposition T-Copula peak load VaR DBN |
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