Estimation of electric power consumption using nighttime light remote sensing data based on K-Means city classification algorithm
ZHANG Li1,2(), XIE Yanan1, QU Chenyang2,3, WANG Mingquan2,3, CHANG Zheng2,3,4, WANG Maohua2,3()
1. 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 2. Shanghai Carbon Data Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China 3. CAS Key Laboratory of Low-Coal Conversion Science and Engineering, Shanghai Advanced Research Institute, Shanghai 201210, China 4. Dalian National Laboratory for Clean Energy, Dalian 116023, China
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.
张莉, 谢亚楠, 屈辰阳, 汪鸣泉, 常征, 王茂华. 基于K-Means城市分类算法的夜光遥感电力消费估算[J]. 国土资源遥感, 2020, 32(4): 182-189.
ZHANG Li, XIE Yanan, QU Chenyang, WANG Mingquan, CHANG Zheng, WANG Maohua. Estimation of electric power consumption using nighttime light remote sensing data based on K-Means city classification algorithm. Remote Sensing for Land & Resources, 2020, 32(4): 182-189.
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2020, Vol. 32 
), 谢亚楠1, 屈辰阳2,3, 汪鸣泉2,3, 常征2,3,4, 王茂华2,3(
