Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

Ren, Simiao; Hu, Wayne; Bradbury, Kyle; Harrison-Atlas, Dylan; Malaguzzi Valeri, Laura; Murray, Brian; Malof, Jordan M.

doi:10.1016/j.apenergy.2022.119876

Title: Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

Journal Article · 01 September 2023 · Applied Energy

DOI: https://doi.org/10.1016/j.apenergy.2022.119876 · OSTI ID:1903025

Ren, Simiao ^[1]; Hu, Wayne ^[1]; Bradbury, Kyle ^[1]; Harrison-Atlas, Dylan ^[2]; Malaguzzi Valeri, Laura ^[3]; Murray, Brian ^[1]; Malof, Jordan M. ^[4]

Duke University, Durham, NC (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
World Resources Institute, Washington, D.C (United States)
University of Montana, Missoula, MT (United States)

We report high quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE; national Science Foundation (NSF)

Grant/Contract Number:: AC36-08GO28308; OIA-1937137

OSTI ID:: 1903025

Report Number(s):: NREL/JA-6A20-77734; MainId:30649; UUID:167efa42-0395-4333-9a98-95f3542e3e16; MainAdminID:68203

Journal Information:: Applied Energy, Vol. 326; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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