Use of smart meter readings and nighttime light images to track pixel-level electricity consumption

Deng, Chengbin; Lin, Weiying; Chen, Shili

doi:10.1080/2150704x.2018.1538582

Cited by 12 publications

(8 citation statements)

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“…Nighttime lighting (NTL) data can reflect human activities and economic development, and is comprehensively applied to socio-economic parameter estimation [ 26 , 27 , 28 ], population distribution estimation [ 29 , 30 ], energy and power consumption [ 31 , 32 , 33 ], urbanization monitoring [ 34 , 35 , 36 ], and urban agglomeration evolution [ 37 , 38 ] which has become a major source for monitoring human activities and socio-economic development. Numerous studies adopted nighttime light data to extract urban scope and measure urbanization level, and their results show that nighttime light data can characterize urbanization and urban expansion [ 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatiotemporal Changes of Ecological Environment Quality and Its Coupling Coordination with Urbanization in the Yangtze River Delta Urban Agglomeration, China

Shi

Wang

Zhao

2023

IJERPH

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It is inevitable that urban agglomeration will have a coercive impact on the regional Ecological Environment Quality (EEQ) as a consequence of high-speed urbanization. Balancing the EEQ and urbanization development has become a problem worthy of attention. In order to objectively evaluate the EEQ of the Yangtze River Delta Urban Agglomeration (YRDUA) and explore the impact of the urbanization process on it, this paper is based on the Modified Remote Sensing Ecological Index (MRSEI) and the Comprehensive Night Light Index (CNLI), respectively, and conducts a quantitative assessment of the YRDUA in China from 2000 to 2020. The results show that: (1) From 2000 to 2020, the MRSEI of the YRDUA first decreased and then increased, and the ecological environment quality degraded first and then improved; however, there were significant differences between regions. The ecological environment quality in the south is obviously better than that in the north, and the ecological environment quality in the north changes more drastically, and the low value area of MRSEI will gradually move downstream as time changes; (2) During the study period, the YRDUA formed a hierarchical and progressive urbanization pattern. The inland urbanization process expanded from east to west along the Yangtze River, and the urbanization process of coastal cities expanded from Shanghai as the center to the north and south with high-intensity urbanization cities concentrated in Shanghai and its surrounding cities and low-intensity urbanization cities distributed in the western part of the urban agglomeration; (3) The Coupling Coordination Degree (CCD) between urbanization and EEQ in the YRDUA has continuously improved with an increase of 28.57% in the past 21 years, and the number of cities with high level coupling continues to rise, while the number of medium level coupling cities and low level coupling cities has declined. As a large-scale and long-term analysis of changes in the EEQ and the urbanization process, this study can provide theoretical support for policymakers to formulate mesoscale development planning, EEQ monitoring, and environmental protection policies.

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Section: Introductionmentioning

confidence: 99%

Analysis of Spatiotemporal Changes of Ecological Environment Quality and Its Coupling Coordination with Urbanization in the Yangtze River Delta Urban Agglomeration, China

Shi

Wang

Zhao

2023

IJERPH

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“…As a popular topic in studies of urban sustainability and regional economics, EPC has been widely studied at large scales [11]. At present, EPC mainly uses administrative regions as the basic statistical unit.…”

Section: Introductionmentioning

confidence: 99%

Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

et al. 2021

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In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

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“…However, the available datasets are inadequate to reflect the spatial variability of EC within a region [21]. Clearly, EC data with raster format would be more accurate and objective than statistical data [22,23]. So, it is vital and urgent to develop a suitable method to map EC on the grid scale.…”

Section: Introductionmentioning

confidence: 99%

“…Super saturation and overflow phenomena still occur in some situations. In addition, the data preprocessing is complicated, and previous studies have tended to focus exclusively on large scales, such as regional [23,28,33], global [26,27], and national scales [30,31]. Although some researches have been conducted on small and medium scales [29,32,34], these have mostly been restricted to studying first-tier cities undergoing rapid expansion.…”

Section: Introductionmentioning

confidence: 99%

Detecting Spatiotemporal Dynamic of Regional Electric Consumption Using NPP-VIIRS Nighttime Stable Light Data–A Case Study of Xi’an, China

Guo

Zhang

et al. 2020

IEEE Access

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The available statistics on electric consumption (EC) can hardly show the spatial heterogeneity within political boundaries, and the previous studies paid few attention on the relations between EC and urban planning. The present study developed models between nighttime light values and EC using nighttime light images (NLI) from the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) and statistical EC data from local government of Xi'an. A geographic information system (GIS) was utilized to map the distribution of EC on a grid scale, and the spatiotemporal dynamic of EC was obtained. In addition, a slope model and the EC grading threshold were conducted to determine the urban functional districts (UFD). The results were shown as follows: (1) the relation between TNL and EC is significant positive, and the performance of linear regression model (R 2 =0.76，Root Mean Square Error (RMSE) =9.44×10 4 , Mean Absolute Error (MAE) =6.73×10 3 , Mean Percent Error (MPE) =-3.71%) is better than logarithmic model (R 2 =0.68, RMSE =1.09×10 5 , MAE =7.77×10 4 , MPE =-5.44%). (2) In Xi'an, the EC continuously increased during 2013-2017, and the change of EC demonstrated obvious features that increased slowly in the city center, and spread outward to the surrounding region especially to the south of the study area. (3) The UFD have been divided into five sections including tourist district, commercial district, industrial zone, residential community, and ecological zone, respectively. This study will thus provide a reference and scientific basis for urban planning and rational allocation of electric power.

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Use of smart meter readings and nighttime light images to track pixel-level electricity consumption

Cited by 12 publications

References 12 publications

Analysis of Spatiotemporal Changes of Ecological Environment Quality and Its Coupling Coordination with Urbanization in the Yangtze River Delta Urban Agglomeration, China

Analysis of Spatiotemporal Changes of Ecological Environment Quality and Its Coupling Coordination with Urbanization in the Yangtze River Delta Urban Agglomeration, China

Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

Detecting Spatiotemporal Dynamic of Regional Electric Consumption Using NPP-VIIRS Nighttime Stable Light Data–A Case Study of Xi’an, China

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