Power Forecasting of Photovoltaic Power Generation System

Pan WU

doi:10.12096/j.2096-4528.pgt.19113

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Power Forecasting of Photovoltaic Power Generation System

Distributed Energy System | 更新时间：2025-07-04

- Power Forecasting of Photovoltaic Power Generation System
- Power Generation Technology Vol. 41, Issue 3, Pages: 231-236(2020)
- 作者机构：
  
  国网浙江台州市路桥区供电有限公司, 浙江省台州市 318050
- 作者简介：
- 基金信息：
- DOI：10.12096/j.2096-4528.pgt.19113
  CLC： TM615
- Received：13 November 2019，
  
  Published：30 June 2020
- 稿件说明：
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Pan WU. Power Forecasting of Photovoltaic Power Generation System[J]. Power Generation Technology, 2020, 41(3): 231-236.
DOI：

Pan WU. Power Forecasting of Photovoltaic Power Generation System[J]. Power Generation Technology, 2020, 41(3): 231-236. DOI： 10.12096/j.2096-4528.pgt.19113.

摘要

为解决光伏发电系统发电功率在不同条件下误差较大问题，提出光伏发电系统发电功率预测新方法。通过分析光伏发电系统结构，研究光伏发电系统发电功率影响因素；以季节和天气类型作为历史样本选取样本源，针对气象部门提供的预测日分时气象数据在历史数据库中寻找相似数据点作为历史样本；依据历史样本构建离线参数寻优数据总集，使用核函数极限学习机算法构建发电系统发电功率预测模型，通过粒子群算法优化模型参数。实验结果表明：所提方法在不同条件下预测太阳能光伏发电系统发电功率的平均绝对百分比误差分别为1.47%和6.39%，光伏组件在综合异常条件下发电功率预测误差相对变化均低于1%，证明所提方法满足实际预测要求。

Abstract

In order to solve the problem of large errors in the power generation of solar photovoltaic system under different conditions

a new method for power generation prediction of solar photovoltaic system was proposed. By analyzing the structure of solar photovoltaic power generation system

the influencing factors of solar photovoltaic power generation system were studied. Seasons and weather types were used as historical samples to select sample sources

and similar data points were searched in the historical database for predicting daily time-sharing meteorological data provided by meteorological departments as historical samples. The off-line parameter optimization data set was constructed with historical samples

and the generation power prediction model of power generation system was constructed with the kernel function limit learning machine algorithm

and the model parameters were optimized by the particle swarm optimization algorithm. The experimental results show that the mean absolute percent errors of the proposed method are 1.47% and 6.39% respectively under different conditions

and the relative variation of the power prediction errors of solar photovoltaic modules is less than 1% under comprehensive abnormal conditions. It is proved that the proposed method meets the actual prediction requirements.

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