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Related Experiment Video

Updated: Dec 3, 2025

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A novel classification regression method for gridded electric power consumption estimation in China.

Mulin Chen1,2, Hongyan Cai3, Xiaohuan Yang1

  • 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing, 100101, China.

Scientific Reports
|October 30, 2020
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Summary

This study introduces a new classification regression method to accurately estimate gridded electric power consumption (EPC) using nighttime light (NTL) data. The method accounts for spatial variations, outperforming traditional approaches for better energy management.

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Area of Science:

  • Remote Sensing
  • Geographic Information Systems (GIS)
  • Energy Economics

Background:

  • Accurate spatially explicit electric power consumption (EPC) data is vital for efficient energy allocation and utilization.
  • Existing methods often overlook the spatial non-stationary relationship between EPC and nighttime light (NTL).
  • Remotely sensed NTL data, particularly from the Visible Infrared Imaging Radiometer Suite (VIIRS), is commonly used for fine-scale EPC estimation.

Purpose of the Study:

  • To develop and evaluate a novel classification regression method for estimating gridded EPC in China.
  • To address the spatial non-stationary relationship between EPC and NTL.
  • To improve the accuracy of EPC estimation compared to existing methods.

Main Methods:

  • Utilized Visible Infrared Imaging Radiometer Suite (VIIRS) NTL data for EPC estimation.
  • Employed cubic Hermite interpolation to refine NTL data, addressing inherent omissions.
  • Developed a classification regression model that incorporates spatial non-stationarity.
  • Compared the proposed method against Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR).

Main Results:

  • The proposed classification regression method demonstrated superior performance over OLS and GWR.
  • Achieved lower relative error (RE) and mean absolute percentage error (MAPE) compared to benchmark methods.
  • The enhanced accuracy is attributed to a classification scheme that effectively captures spatial variations in the EPC-NTL relationship.

Conclusions:

  • The developed classification regression method significantly enhances the accuracy of gridded EPC estimation.
  • This approach provides a valuable predictive model for electricity consumption analysis.
  • Accounting for spatial non-stationarity is crucial for reliable EPC mapping using NTL data.