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基于DKDE与改进mRMR特征选择的短期光伏出力预测
作者:
作者单位:

1.陕西省智能电网重点实验室(西安交通大学电气工程学院),陕西省西安市 710049;2.人工智能与数字经济广东省实验室,广东省广州市 510320

摘要:

随着光伏发电装机容量的增长,其在能源消费中的占比不断提升,准确预测光伏发电功率对电力系统发展规划和调度运行均具有重要意义。目前,针对光伏预测特征选择的研究比较少,不合理的特征选择往往导致信息丢失,气象参数与出力间的映射关系难以有效挖掘,最终导致预测精度偏低。因此,文中提出一种基于改进互信息计算与改进最大相关最小冗余(mRMR)的光伏预测特征选择方法。针对连续随机变量相关性互信息难以直接计算的问题,基于扩散核密度估计(DKDE)理论,提出一种依据概率密度的区间划分方法并应用于变量离散化,以提高互信息对实际有限数据集的表征能力。然后,对传统mRMR的增量搜索过程进行了改进,提出一种可并行筛选多个特征子集的改进mRMR算法,并针对各特征子集分别采用XGBoost算法构建气象信息与光伏功率的预测模型。最后,通过实际光伏电站测量数据验证了所提方法的有效性和准确性。

关键词:

基金项目:

国家重点研发计划资助项目(2018YFB0905000);国家电网公司科技项目(SGTJDK00DWJS1800232)。

通信作者:

作者简介:

刘嘉诚(1998—),男,硕士研究生,主要研究方向:电力系统运行分析和控制、人工智能与机器学习在电力系统中的应用。E-mail:ljc19980227@stu.xjtu.edu.cn
刘俊(1982—),男,通信作者,博士,副教授,主要研究方向:电力系统运行分析和控制、电力系统稳定性、电力系统并行计算、可再生能源并网、HVDC和FACTS技术等。E-mail:eeliujun@mail.xjtu.edu.cn
赵宏炎(1995—),男,硕士研究生,主要研究方向:电力系统运行分析和控制、人工智能在电力系统中的应用。 E-mail:laozhao1213@stu.xjtu.edu.cn


Short-term Photovoltaic Output Forecasting Based on Diffusion Kernel Density Estimation and Improved Max-relevance and Min-redundancy Feature Selection
Author:
Affiliation:

1.Shaanxi Key Laboratory of Smart Grid (School of Electrical Engineering, Xi’an Jiaotong University), Xi’an 710049, China;2.Guangdong Lab of Artificial Intelligence and Digital Economy, Guangzhou 510320, China

Abstract:

With the growth of the installed capacity of the photovoltaic (PV) power generation, its proportion in energy consumption keeps increasing. Accurate forecasting of PV power generation is of great significance to the development plan and dispatching operation of power systems. At present, there are still few researches on feature selection in PV forecasting. Unreasonable feature selection often leads to loss of information, and it is difficult to effectively mine the mapping relationship between meteorological parameters and the power output, which results in low forecasting accuracy. Therefore, this paper proposes a feature selection method for PV forecasting based on improved mutual information calculation and improved max-relevance and min-redundancy (mRMR). Aiming at the problem that it is difficult to directly calculate the correlation and mutual information of continuous random variables, based on the theory of diffusion kernel density estimation (DKDE), an interval division method based on the probability density is proposed and applied to the discretization of variables, which improves the ability of mutual information to represent actual limited data sets. Then, the incremental search process of the traditional mRMR is improved,and an improved mRMR algorithm is proposed, which can select multiple feature subsets in parallel. The XGBoost (eXtreme gradient boosting) algorithm is applied to each feature subset to construct the weather information and PV power forecasting model. Finally, the effectiveness and accuracy of the proposed method are verified by the measured data of an actual photovoltaic power station.

Keywords:

Foundation:
This work is supported by National Key R&D Program of China (No. 2017YFB0905000) and State Grid Corporation of China (No. SGTJDK00DWJS1800232).
引用本文
[1]刘嘉诚,刘俊,赵宏炎,等.基于DKDE与改进mRMR特征选择的短期光伏出力预测[J].电力系统自动化,2021,45(14):13-21. DOI:10.7500/AEPS20201126004.
LIU Jiacheng, LIU Jun, ZHAO Hongyan, et al. Short-term Photovoltaic Output Forecasting Based on Diffusion Kernel Density Estimation and Improved Max-relevance and Min-redundancy Feature Selection[J]. Automation of Electric Power Systems, 2021, 45(14):13-21. DOI:10.7500/AEPS20201126004.
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  • 收稿日期:2020-11-26
  • 最后修改日期:2021-02-20
  • 录用日期:
  • 在线发布日期: 2021-07-21
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