文章摘要
林晶怡,王占博,许一川,刘 畅,谈 诚.环境气候变化对渔业电气设备负荷影响研究[J].电力需求侧管理,2024,26(4):107-112
环境气候变化对渔业电气设备负荷影响研究
Study on the influence of environmental climate change on the load of fishery electrical equipment
投稿时间：2024-04-15  修订日期：2024-05-25
DOI：10. 3969 / j. issn. 1009-1831. 2024. 04. 017
中文关键词: 渔光储  气象负荷  主成分分析法  BP神经网络  负荷预测
英文关键词: fisheries, photovoltaics and energy storage  meteorological load  principal component analysis method  BP neural network  load forecasting
基金项目:国家电网有限公司总部科技项目（5400-202218162A-1-1-ZN）
作者单位
林晶怡 中国电力科学研究院有限公司,北京 100192 
王占博 中国电力科学研究院有限公司,北京 100192 
许一川 国网江苏省电力有限公司 常州供电分公司,江苏 常州 213003 
刘 畅 中国电力科学研究院有限公司,北京 100192 
谈 诚 国网江苏省电力有限公司 常州供电分公司,江苏 常州 213003 
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中文摘要:
      随着智能渔场电气化水平不断提高,渔场内环境敏感型负荷比例增加,负荷预测应充分考虑环境气候变化对渔业电气设备运行的影响。首先,对渔场监测平台采集的数据进行分析与预处理,依据气候特征进行典型场景划分；其次,利用最小二乘法分解受环境气候影响的气象负荷,采用相关系数法分析负荷与气象指标之间的相关性,通过SPSS软件采用主成分分析法将多个单一气象指标转化为少数几个综合指标,利用综合气象指标和气象负荷数据进行BP神经网络训练,构建了典型场景下基于BP神经网络的渔业电气设备负荷预测模型；最后,基于目标日的渔业电气设备运行情况和气候指标数据进行了实例验证,结果表明该模型反映了环境气候变化对渔业电气设备负荷的影响,误差精度满足实际工程需要。
英文摘要:
      With the continuous improvement of the electrification level of smart fishing grounds, the proportion of environmentally sensi?tive loads in fishing grounds increases. Load forecasting should fully consider the impact of environmental climate change on the operationof fishery electrical equipment. Firstly, the data collected by the fishing ground monitoring platform are analyzed and preprocessed, andthe typical scenes are divided according to the climatic characteristics. Secondly, the least square method is used to decompose the meteo?rological load affected by the climate, and the correlation coefficient method is used to analyze the correlation between the load and the me?teorological index. The principal component analysis method is used to transform multiple single meteorological indicators into a few com?prehensive indicators in SPSS. The comprehensive meteorological indicators and meteorological load data are used to train the BP neuralnetwork, and the load forecasting model of fishery electrical equipment based on BP neural network in typical scenarios is constructed. Fi?nally, an example is verified based on the operation of fishery electrical equipment and climate index data on the target day. The resultsshow that the model reflects the impact of environmental climate change on the load of fishery electrical equipment, and the error accuracymeets the actual engineering needs.
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