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气象:2020,46(9):1245-1253
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气象因子对夏季最大电力负荷的敏感性分析
任永建,熊守权,洪国平,程定芳
(湖北省气象服务中心,武汉 430205; 武汉区域气候中心,武汉 430074; 湖北省黄冈市气象服务中心,黄冈 438000)
Sensitivity Analysis of Meteorological Factors to Summer Maximum Power Load
REN Yongjian,XIONG Shouquan,HONG Guoping,CHENG Dingfang
(Hubei Meteorological Service Center, Wuhan 430205; Wuhan Regional Climate Center, Wuhan 430074; Huanggang Meteorological Service Center of Hubei Province, Huanggang 438000)
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投稿时间:2019-06-13    修订日期:2020-05-15
中文摘要: 利用2016—2018年武汉夏季(6—9月)逐15 min电力负荷以及同期逐日气象数据,分析最大电力负荷变化特征及与气象因子的相关关系。利用逐步回归和双隐含层BP神经网络算法,建立了武汉夏季最大电力负荷的预测模型。结果表明:平均温度、平均最高温度、平均最低温度与气象电力负荷存在显著的正相关,其次是日照时数。前1 d最大电力负荷与当日最大电力负荷的相关性最好,当日电力负荷对前1 d温度的平均和舒适度指数的变化最为敏感。以历史电力负荷和气象数据为联合预报因子,逐步回归和BP神经网络算法对武汉夏季最大电力负荷具有较好的模拟效果,尤其是对持续高温造成高位运行的最大负荷模拟。当敏感性在10%以内时,逐步回归算法中气象因子正的贡献要小于负的贡献,BP神经网络算法中气象因子正的贡献要高于负的贡献;当敏感性高于10%时,两种算法中气象因子均为正的贡献。
Abstract:The summer maximum power load variation and its correlation with meteorological factors were analyzed by using the 15 min power load and the daily meteorological data in Wuhan from 2016 to 2018. Based on stepwise regression and double hidden layer BP neural network algorithm, the prediction model of summer maximum power load was established. The results show that there is a significant positive correlation between the mean temperature, average maximum temperature and average minimum temperature and the meteorological load. The correlation between the maximum load on the day and the load on the previous day is the best. The load on the day is most sensitive to the average temperature and comfort index in the previous two days. With historical load and meteorological data as joint forecasting factors, the stepwise regression and BP neural network algorithm have a good simulation effect on the maximum summer power load in Wuhan, especially on the maximum load of high-level operation caused by continuous high temperature in 2018. When the sensitivity is lower than 10%, the positive contribution of the meteoro-logical factor in the stepwise regression algorithm is less than the negative contribution, and the positive contribution in the BP neural network algorithm is higher than the negative contribution. But when the sensitivity is higher than 10%, the meteorological factors in both algorithms contribute positively.
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基金项目:湖北省气象局科技基金重点项目(2019Z09)、中国气象局气候变化专项(CCSF202033、CCSF202008和CCSF201821)共同资助
引用文本:
任永建,熊守权,洪国平,程定芳,2020.气象因子对夏季最大电力负荷的敏感性分析[J].气象,46(9):1245-1253.
REN Yongjian,XIONG Shouquan,HONG Guoping,CHENG Dingfang,2020.Sensitivity Analysis of Meteorological Factors to Summer Maximum Power Load[J].Meteor Mon,46(9):1245-1253.