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ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (6): 94-98.DOI: 10.19912/j.0254-0096.tynxb.2020-1007

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基于MIV-PSO-BPNN的光伏出力短期预测

刘丹1,2, 刘方1,2, 许彦平1   

  1. 1.新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司),北京 100192;
    2.上海电力大学能源与机械工程学院,上海 200090
  • 收稿日期:2020-09-20 出版日期:2022-06-28 发布日期:2022-12-28
  • 通讯作者: 刘 方(1974—),女,博士、教授,主要从事新能源系统优化、热力循环与系统优化方面的研究。fangliu@shiep.edu.cn
  • 基金资助:
    新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司)开放基金资助(NYB51202001608)

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON MIV-PSO-BPNN MODEL

Liu Dan1,2, Liu Fang1,2, Xu Yanping1   

  1. 1. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute, Beijing 100192, China;
    2. College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
  • Received:2020-09-20 Online:2022-06-28 Published:2022-12-28

摘要: 针对高比例光伏接入电网时,光伏出力的波动性会严重影响电力系统稳定运行的问题提出一种基于平均影响值与改进粒子群优化神经网络的组合式光伏出力短期预测模型。首先,采用直接预测法,选取总辐射量、直接辐射量、散射量、相对湿度、气温、风速和降雨量7个影响光伏出力的因素,构建MIV-PSO-BPNN模型,基于Rapid Miner数据挖掘得出降雨量对光伏出力平均影响值为0.0099,影响较小,不作为模型输入变量。然后,用改进PSO优化算法对BPNN的权值与阈值进行优化。最后,利用上海浦东国际机场T2-2光伏电站数据进行验证,结果表明MIV-PSO-BPNN模型对光伏出力预测有效,在实际中有一定应用价值。

关键词: 光伏发电, 数据挖掘, 粒子群优化算法, 神经网络, 平均影响值, 权值与阈值, 短期预测

Abstract: Aiming at the fluctuation of photovoltaic output will seriously affect the stable operation of power system when a high proportion of PV is connected to the power grid, a combined photovoltaic output short-term prediction model based on mean impact value and improved particle swarm optimization neural network was proposed. Firstly, the MIV-PSO-BPNN model was developed by selecting the total radiation, direct radiation, scattering radiation, relative humidity, temperature, wind speed and rainfall, as the main factors affecting on the PV power generation. Based on the Rapid Miner data mining, the mean impact value of rainfall on PV output is 0.0099, the influence is small and is not considered as the model input variables. Then, the improved particle swarm optimization (PSO) was applied to optimize the weights and thresholds of BP neural networks. Finally, the data of photovoltaic power station of Shanghai T2-2 Pudong International Airport was used for verification. The comparison results show that the MIV-PSO-BP neural network model can used effectively for PV power generation prediction, and has application value in practice.

Key words: PVpower, data mining, particle swarm optimization, neural networks, mean impact value, weight and threshold, short-term forecasting

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