New Machine Learning Findings from University of Washington Described [From Tweets To Energy Trends (Twen): an Exploratory Framework for Machine Learni ng-based Forecasting of Urban-scale Energy Behavior Leveraging Social Media Data ]

华盛顿大学的新机器学习发现描述[从推特到能源趋势(Twen)：一项探索性研究基于机器学习的城市规模预测框架利用社交媒体数据的能源行为]

Robotics & Machine Learning Daily News2024，Issue(Nov.8) ：16-16.

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New Machine Learning Findings from University of Washington Described [From Tweets To Energy Trends (Twen): an Exploratory Framework for Machine Learni ng-based Forecasting of Urban-scale Energy Behavior Leveraging Social Media Data ]

华盛顿大学的新机器学习发现描述[从推特到能源趋势(Twen)：一项探索性研究基于机器学习的城市规模预测框架利用社交媒体数据的能源行为]

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的最新研究结果已经发表。据新闻报道来自华盛顿州西雅图的报道，由NewsRx记者报道，研究称，“理解”能源在应对气候变化方面至关重要，然而，离子能量预测的准确性往往是受过于简化的占用数据的限制。本研究建立了一个探索性的框架，从Tweets到Energy Trends(TwEn)、leveragin g机器学习和地理标记的社交媒体数据到调查城市能源行为的社会动力学。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting originating from Seattle, W ashington, by NewsRx correspondents, research stated, “Understandingenergy beha vior is crucial in addressing climate change, yet the accuracy of energy predict ions is oftenlimited by reliance on oversimplified occupancy data. This study d evelops an exploratory framework,from Tweets to Energy Trends (TwEn), leveragin g machine learning and geo-tagged social media data toinvestigate the social dy namics of urban energy behavior.”

Key words

Seattle/Washington/United States/Nort h and Central America/Cyborgs/Emerging Technologies/Machine Learning/Univers ity of Washington

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出版年

2024

Robotics & Machine Learning Daily News

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