基于相似日算法及集成学习的短期光伏预测模型

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摘要随着分布式发电系统的日益增多,光伏发电预测逐渐成为影响电力系统运行及调度的关键。本文提出一种基于改进相似日算法和集成学习的短期光伏发电混合预测模型。应用改进的相似日算法在历史数据中找到相似日,将相似日数据和气候因素等作为Bagging集成学习的输入变量,对其进行建模训练。通过公开的光伏数据集进行验证,并与传统的神经网络模型和支持向量机进行对比。结果表明,该模型具有较高的预测精度。

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	武明义
	焦超凡
	瞿博阳
	焦岳超
	付凯

关键词 ：光伏发电预测, 相似日算法, 集成学习, 混合预测模型

Abstract：With the increasing of the distributed generation in the power system, photovoltaic power forecasting has become essential in the planning and operation of the electric power system. This paper proposes a hybrid model which utilizing the improved similarity day algorithm and the Bagging ensemble learning for short-term photovoltaic power forecasting. By using the improved similarity day algorithm, the similar day is found out from the history data of photovoltaic output. The similar day and other climate factor make up the input vector of the decision tree model, which has been trained by using the Bagging ensemble learning algorithm. To confirm the effectiveness of the proposed modeling strategy, the model has been tested on the publicly available data set of photovoltaic output and compared with the classical neural network model and SVM. The results of the proposed model show a better accuracy.

Key words： photovoltaic power forecasting similar day algorithm ensemble learning hybrid prediction model

收稿日期: 2020-09-01

基金资助:国家自然科学基金资助项目（61673404,61976237）; 河南省高等学校重点科研项目（19A120014,20A120013）; 2019中原工学院青年人才创新能力基金项目（K2019QN005）

作者简介: 武明义（1985—）,男,讲师,主要研究方向为智能电网与新能源发电。

引用本文:

武明义, 焦超凡, 瞿博阳, 焦岳超, 付凯. 基于相似日算法及集成学习的短期光伏预测模型[J]. 电气技术, 2021, 22(4): 33-37. WU Mingyi, JIAO Chaofan, QU Boyang, JIAO Yuechao, FU Kai. Short-term photovoltaic power forecasting model based on similarity day algorithm and ensemble learning. Electrical Engineering, 2021, 22(4): 33-37.

链接本文:

http://dqjs.cesmedia.cn/CN/Y2021/V22/I4/33

[1] 葛乐, 陆文伟, 袁晓冬, 等. 基于改进相似日和ABC-SVM的光伏电站功率预测[J]. 太阳能学报, 2018, 39(3): 775-782.
[2] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6):1201-1217.
[3] 史佳琪, 张建华. 基于多模型融合Stacking集成学习方式的负荷预测方法[J]. 中国电机工程学报, 2019, 39(14): 4032-4041.
[4] 陈昌松, 段善旭, 殷进军. 基于神经网络的光伏阵列发电预测模型的设计[J]. 电工技术学报, 2009, 24(9): 153-158.
[5] 李燕斌, 万亚宁, 肖俊明, 等. 基于PSO优化BP神经网络的光伏发电量预测[J]. 中原工学院学报, 2019, 30(4): 75-79.
[6] 商立群, 朱伟伟. 基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法[J]. 电工技术学报, 2019, 34(12): 2606-2614.
[7] 宋学伟, 刘天羽, 江秀臣, 等. 基于改进鱼群算法与最小二乘支持向量机的短期负荷预测[J]. 电气技术, 2019, 20(11): 20-26.
[8] 郭国太. 居民区用电负荷特性研究与模型预测[J]. 电气技术, 2020, 21(1): 41-46, 53.
[9] 魏波, 胡海涛, 王科, 等. 基于实测数据和行车运行图的高铁牵引变电站负荷预测方法[J]. 电工技术学报, 2020, 35(1): 179-188.
[10] 潘文虎, 夏友斌, 宋铭敏, 等. 基于累积平均气温模型的气象负荷预测[J]. 电气技术, 2020, 21(4): 67-70.
[11] WANG Kejun, QI Xiaoxia, LIU Hongda.Photovoltaic power forecasting based LSTM-convolutional net-work[J]. Energy, 2019, 189: 116-225.
[12] BARMAN M, CHOUDHURY N B D, SUTRADHAR S. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India[J]. Energy, 2018, 145: 710-720.
[13] MUHAMMAD W A, MONJUR M, YACINE R.Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression[J]. Energy, 2018, 164: 465-474.
[14] 吴瑞林, 王建中, 袁克海. 多分格相关与皮尔逊相关的蒙特卡罗仿真[J]. 北京航空航天大学学报, 2009, 35(12): 1507-1510, 1515.
[15] LITJENS G B M A, WORRELL E, SARK W G J H M V. Assessment of forecasting methods on performance of photovoltaic-battery systems[J]. Applied Energy, 2018, 221: 358-373.
[16] YING C, PETER B L, CHE G, et al.Shortterm load forecasting: similar day-based wavelet neural net-works[J]. IEEE Transactions on Power Systems, 2010, 25(1): 322-330.
[17] 何锋, 章义军, 章建华, 等. 基于相似日和分位数回归森林的光伏发电功率概率密度预测[J]. 热力发电, 2019, 48(7): 64-69.