[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126526-en":3,"doc-seo-126526-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126526,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments","Unmanned aerial vehicles integrate into modern IoT and CPS settings for industrial, military, and entertainment use, making energy consumption prediction essential. The study proposes a machine learning method to predict UAV power usage at specific flight times, avoiding complex, time-consuming measurements in prescribed environments. The approach is fast and easy to implement, predicting real-world power consumption in five classes with a balanced accuracy of 66.7%.","Proceedings of the 56th Hawaii International Conference on System Sciences | 2023  \nMachine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments  \nJulian Gatscher University of Bayreuth [julian.gatscher@uni-bayreuth.de](julian.gatscher@uni-bayreuth.de)  \nJohannes Breitenbach University of Bayreuth [johannes.breitenbach@uni-bayreuth.de](johannes.breitenbach@uni-bayreuth.de)  \nRicardo Buettner University of Bayreuth Fraunhofer FIT  \n[ricardo.buettner@uni-bayreuth.de](ricardo.buettner@uni-bayreuth.de)  \nAbstract  \nUnmanned aerial vehicles are becoming integrated into a wide range of modern IoTand CPS environments for various industrial, military, and entertainment applications. With growing estimations for this market in the future, the problem of energy consumption and its prediction is becoming increasingly important for optimal battery-saving, as well as the safety of the application and thus protection of surrounding persons near the drone flight. This paper presents a machine learning-based approach for the prediction of the power consumption of unmanned aerial vehicles at certain times of the flight. Instead of predicting the power consumption in prescribed environments with complex, time-consuming measurement techniques, our approach is fast, easy to implement, and predicts real-world power consumption in five classes, with a balanced accuracy of 66.7 percent.  \nKeywords: UAV, drones, power consumption, machine learning, dynamic environments  \n1. Introduction  \nThe exceptional growth of the unmanned aerial vehicle (UAV) sales is expected to continue with shipments of over 90 million consumer UAVs to be recorded in 2025 alone (Yuan et al., 2018) . UAVs are nowadays used for various applications, like mailing, delivery of products, inspection of hard-to-reach areas like pipelines and bridges, military usage, and other industrial applications (Hassanalian & Abdelkefi, 2017; Lee & Choi, 2016) . The benefit of using UAVs for companies is having lower cost, increased speed, and reduced greenhouse gas emissions (Zhang et al., 2021) . Additionally, for consumers, the usage of drones for  \nentertainment becomes more and more popular (Quiroz & Kim, 2017) .  \nWith the increasing use of drones and the population density rising, a new problem, besides surveillance fear of inhabitants, arose. Namely, the fear of falling drones causing physical injuries to people (Dalamagkidis et al., 2008; Schenkelberg, 2016) . Not only does the weight significantly impact the potential damage caused by falling drones, but also the propeller blades can harm persons.  \nThere were over 4,250 registered drone injuries between 2015 and 2020 (Gorucu & Ampatzidis, 2021) . With over 70 percent, most injury diagnoses were lacerations, followed by contusion or abrasion with about ten persons, and strain and internal injuries each with 5 percent. The most injured body parts are the fingers and the head of victims. The impacts on the body can therefore be drastic (Dalamagkidis et al., 2008; Duma et al., 2021; Gorucu & Ampatzidis, 2021), which leads to the first researchers assessing the risk of drone flights (Dalamagkidis et al., 2008) .  \nWith growing estimations for the UAV market, this number of accidents is also expected to grow in the future (Clothier et al., 2015; Giones & Brem, 2017) . One major issue causing this is the fact that most regulations are still superficial (Dalamagkidis et al., 2008) . This is why damage prevention by falling drones is important (Schenkelberg, 2016) and should be implemented by developers. As stated previously, the technology of drones is developing rapidly, but safety regulations do not (Dalamagkidis et al., 2008; Zhang et al., 2021) .  \nLimited battery life and its estimation are oneof the biggest challenges in drone development (Abeywickrama et al., 2018; Mansouri et al., 2017) . This fact also significantly influences the emergency  \nURI: [https://hdl.handle.net/10125/103473](http","cbCaiiHqzqUkTLIw","https://ap.wps.com/l/cbCaiiHqzqUkTLIw","pdf",199892,2,1,10,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the paper address for UAV operations?\",\"answer\":\"The paper targets energy consumption prediction for unmanned aerial vehicles, which is important for battery savings, application safety, and risk protection for people near drone flight paths.\"},{\"question\":\"How does the proposed method differ from traditional measurement-based approaches?\",\"answer\":\"Instead of relying on complex, time-consuming measurements in predefined environments, it uses a machine learning approach to predict real-world power consumption.\"},{\"question\":\"How accurate is the prediction method?\",\"answer\":\"The approach predicts power consumption in five classes and achieves a balanced accuracy of 66.7%.\"}]","Machine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments | 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problem does the paper address for UAV operations?","Question",{"text":76,"@type":77},"The paper targets energy consumption prediction for unmanned aerial vehicles, which is important for battery savings, application safety, and risk protection for people near drone flight paths.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method differ from traditional measurement-based approaches?",{"text":81,"@type":77},"Instead of relying on complex, time-consuming measurements in predefined environments, it uses a machine learning approach to predict real-world power consumption.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate is the prediction method?",{"text":85,"@type":77},"The approach predicts power consumption in five classes and achieves a balanced accuracy of 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