摘要
为了减小利用夜间灯光影像估算城市电力消费量时的误差,需要考虑样本地区的发展状况,在估算之前对样本进行分类。选取2015年中国大陆263个地级市的NPP-VIIRS夜间灯光数据对城市电力消费量进行估算。提出了基于灯光结构而非传统统计数据的K-Means城市分类算法。利用该方法将样本分为5类并估算电力消费量,与其他分类方法的估算结果相比可知:该方法估算值的平均相对误差和均方根误差分别为32.02%和57.04,较不分类时分别减小25和3.39百分点;估算中的高精度城市比例为53.99%,较不分类时增加了13.59百分点,且为所有方法中的最高比例;相较不分类时的估算结果,有152个城市的估算误差有所降低。该方法性能与其他分类方法的最优性能相似。
In order to reduce the error in estimating urban electric power consumption by nighttime light images,it is necessary to consider the development status of sample areas and classify the samples before estimation.In this paper,the NPP-VIIRS nighttime light data from 263 prefecture-level cities in China’s mainland in 2015 were selected to estimate urban electric power consumption.A K-Means city classification method based on light structure rather than traditional statistical data is proposed.The authors used this method to divide the samples into 5 types and estimate the electric power consumption.A comparison of the estimated results with those from other classification methods shows the following regularity:The mean relative error and root mean square error of the estimated results are 32.02%and 57.04,decreasing by 25 and 3.39 percentage points compared with the estimated results without classification respectively.The proportion of high-precision cities in the estimation results is 53.99%,increasing by 13.59 percentage points compared with estimated result without classification,and is the highest proportion among values of all methods.Compared with the estimated results without classification,152 cities have lower estimated errors.The performance of this method is similar to the optimal performance of other classification methods.
作者
张莉
谢亚楠
屈辰阳
汪鸣泉
常征
王茂华
ZHANG Li;XIE Yanan;QU Chenyang;WANG Mingquan;CHANG Zheng;WANG Maohua(Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 201210, China;Shanghai Carbon Data Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China;CAS Key Laboratory of Low-Coal Conversion Science and Engineering, Shanghai Advanced Research Institute, Shanghai 201210, China;Dalian National Laboratory for Clean Energy, Dalian 116023, China)
出处
《国土资源遥感》
CSCD
北大核心
2020年第4期182-189,共8页
Remote Sensing for Land & Resources
基金
中国科学院洁净能源创新研究院合作基金项目“变革性洁净能源关键技术对我国碳排放达峰目标的贡献及其减排路径研究”(编号:DNL180101)
国家自然科学基金项目“面向低碳城市规划的碳排放评价方法研究”(编号:51778601)
国家重点研发计划项目“行业碳排放核算与效益成本评估模型研究”(编号:2016YFA062603)
国家重点研发计划项目“基于碳卫星数据的全球大气中CO2浓度估算与预测模型研究”(编号:2016YFA062602)
国家重点研发计划项目“世界主要国家碳排放因子研究”(编号:2017YFA065300)共同资助。
