| 唐伟斌,李涛,邹品晶,曾建平,向仲卿,胡斯佳.基于皮尔逊相关系数的电网夏季空调负荷预测研究[J].电力需求侧管理,2017,19(6):7-13 |
| 基于皮尔逊相关系数的电网夏季空调负荷预测研究 |
| Air.condition load forecasting in summer of power grid usingpearson correlation coefficient |
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| DOI：10.3969/j.issn.1009-1831.2017.06.003 |
| 中文关键词: 空调降温负荷 Pearson相关系数 经济气象综合指标 预测模型 |
| 英文关键词: air.conditioning cooling load Pearson correla.tion coefficient the economic.meteorological comprehensive index forecasting model |
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| 摘要点击次数: 2044 |
| 全文下载次数: 19 |
| 中文摘要: |
| 用统计分析的方式研究地区电网夏季空调负荷的变化规律，综合考虑空调负荷的影响因素，构建了地区电网夏季空调负荷预测模型。该模型以地区电网负荷变化曲线为基础，实现了对空调负荷曲线的有效分离，随后采用 Pearson相关系数考察各类影响因素与日最大空调降温负荷的相关性，通过比较日最大空调降温负荷与各类指标的相关系数，构造出一个能够体现最大空调负荷受本地区气象和社会经济影响较大的“经济气象综合指标”，并利用回归分析提炼出空调降温负荷与综合指标的变化规律，最终实现夏季空调负荷的精准预测。基于某地区电网 2006—2015年电力负荷及其相关数据，使用该模型预测 2016年该地区电网夏季空调负荷，预测结果证明了模型的有效性。 |
| 英文摘要: |
| In this paper, the variation rule of the air.condi.tioning load in summer regional power grid can be obtained usingstatistical analysis.By overall considering the influencing factors ofair.conditioning load, the forecasting model of summer air.condition.ing load in regional power grid was built.The model was based onthe load curves of the regional power grid, and realized the effectiveseparation of the air.conditioning load curve.Then the Pearson cor.relation coefficient was used to investigate the correlation betweenvarious factors and daily maximum air.conditioning cooling load.Through comparing the correlation coefficient between the dailymaximum air .conditioning cooling load and all kinds of indexes, aneconomic .meteorological comprehensive index, reflecting the factthat the daily maximum air.conditioning load is greatly affected bythe meteorological and socio.economic in a region, was constructed.And the regression analysis was used to extract the change rule ofthe air conditioning cooling load and the comprehensive index.Fi.nally, the accurate prediction of summer air.conditioning load is re.alized.A regional data of power load and its related factors from2006 to 2015 was used as training sample to forecast the summer air.conditioning load in 2016 of the region.The forecasting resultsproved the validity of the proposed model. |
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