Building a top-down method based on machine learning for evaluating energy intensity at a fine scale

Energy intensity is an important representative of energy efficiency. Currently, most countries lack finescale energy intensity data, taking China as an example, it only published provincial energy intensity data. However, the published large-scale energy intensity cannot support the formulation of local policies. What's more, the research work about the evaluation of fine-scale energy intensity is rare. To solve this problem, a top-down method based on machine learning is proposed to evaluate the fine-scale energy intensity. Appropriate features were extracted from multi-source satellite data, then the performances of multiple machine learning models were compared. It is found that deep neural network reaches the highest level among these models. Therefore, it was selected to estimate city-scale energy intensity from the year of 2001-2017. It turns out that the energy efficiency of southeast cities is higher than that of northwest cities in China, and most cities are developing towards the direction of improving energy efficiency. Among all cities, the central ones are the fastest to improve energy efficiency. However, the energy efficiency of a few cities is found to reduce during this period. The proposed method can also be used in other countries to help governments save energy and reduce emissions. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Energy intensity, an important indicator of energy efficiency, is lacking finescale data in most countries. This study proposes a top-down method based on machine learning to evaluate the fine-scale energy intensity using features extracted from satellite data. The research finds that southeast cities in China have higher energy efficiency compared to northwest cities, and most cities are developing towards improved energy efficiency. The method can also be applied in other countries to assist governments in energy saving and emissions reduction.