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A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System

机译：基于相关性分析的短期功率输出预测模型和分布式PV系统的ELM-LSTM

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摘要

Accurate short-term power output forecasting results are conducive to reducing the scheduling difficulty of grid-connected operation of distributed photovoltaic (PV) systems, thus improving the safety and stability of power grid operation. In this paper, a one-day-ahead short-term power output forecasting model based on correlation analysis and combination algorithms for distributed PV system is proposed to solve the problems within the current methods. Firstly, the basic information of distributed PV system is introduced, and the main influence factors affecting the power output of distributed PV system are determined. Secondly, the influence factors with higher correlation with PV output are selected by Spearman rank-order correlation coefficient (SROCC) analysis in multiple timescales. Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. The case study analysis based on the actual data indicates that the ELM-LSTM forecasting model proposed in this paper has higher forecasting accuracy than the traditional forecasting methods.
机译：精确的短期功率输出预测结果有利于降低分布式光伏（PV）系统的电网连接操作的调度难度，从而提高了电网运行的安全性和稳定性。本文提出了一种基于相关性分析和分布式PV系统的组合算法的一天前的短期功率输出预测模型，以解决当前方法内的问题。首先，介绍了分布式光伏系统的基本信息，确定了影响分布式PV系统电源输出的主要影响因素。其次，通过在多个时间尺度中的Spearman等级相关系数（SROCC）分析选择具有更高相关性的影响因素。然后，基于多模型单序列极端学习机（ELM）子模型和单模型多变量长短期存储器（LSTM）子模型，建立了ELM-LSTM模型。基于实际数据的案例研究分析表明本文提出的ELM-LSTM预测模型具有比传统预测方法更高的预测精度。

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  • 来源
    《Journal of electrical and computer engineering》 |2020年第1期|2051232.1-2051232.10|共10页
  • 作者

    Deng Yongsheng; Jiao Fengshun; Zhang Jie; Li Zhikeng;

  • 作者单位

    Shenzhen Power Supply Co Ltd Shenzhen 518033 Peoples R China;

    Shenzhen Power Supply Co Ltd Shenzhen 518033 Peoples R China;

    Shenzhen Power Supply Co Ltd Shenzhen 518033 Peoples R China;

    China Energy Engn Grp Guangdong Elect Power Desig Guangzhou Peoples R China;

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