Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis. (15th November 2022)

• Record Type: Journal Article
• Authors: Ren, Simiao; Hu, Wayne; Bradbury, Kyle; Harrison-Atlas, Dylan; Malaguzzi Valeri, Laura; Murray, Brian; Malof, Jordan M.
• Is Part Of: Applied energy. Volume 326(2022)
• Publication Date: 2022-11-15
• Subjects: AP Average precision -- API Application programming interface -- AUC Area under the curve -- DNN Deep neural network -- FPR False positive rate -- IoU Intersection over union -- LST Land surface temperature -- LiDAR Light detection and ranging -- m Meters -- MAE Mean absolute error -- mAP Mean average precision -- ML Machine learning -- MSE Mean square error -- NAIP National Agricultural Imagery Program -- NASA National Aeronautics and Space Administration -- NDVI Normalized difference vegetation index -- NTL Nighttime lights -- PR Precision-recall -- PV Photovoltaic -- REMOTE-ML REMOTely-sensed Energy systems using Machine Learning -- RGB Red, green, blue -- ROC Receiver operating characteristics -- RSD Remote sensing data -- SAR Synthetic aperture radar -- TPR True positive rate -- UAV Unmanned aerial vehicle -- USGS United States Geological Survey; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119876
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Systematic review on remote sensing, machine learning for energy data extraction. Useful energy information has been extracted across the energy value chain. Methods extract new energy information; greater scale and frequency of existing data. Greater rigor in validation of methods is key for trustworthiness in decision-making. Abstract: 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.