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趙梓吟
建築數聚時代
建築數聚時代
建築數聚時代
By
趙梓吟
國立交通大學研究助理
・
Hsinchu City, Taiwan 300
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[負責項目] 機器學習演算法之設計與實踐、案場程式部署、帶領新人 [合作公司] 台積電 [專案目標] 大型辦公場域的設備耗能高,若能掌握室內人員狀況將有利於節能控制。我們旨在利用機器學習藉由綜合當下辦公室內感測器讀值判斷辦公室中人員相較於尖峰的佔比,提供台積作為切換不同環控模式的依據。 [專案績效] 1. 本系統不須精確標記所有時刻相較於尖峰的人員佔比,僅需為約7%總體資料標記離散標籤(尖峰/離峰)即可佈署。 2. 部署後協助節省約2%空調箱用電量,5%外氣空調箱用電量,以及3%照明用電量。 [專案進度] 完成,建置於台積電台中15B廠。 [榮獲獎項] 2020年第13屆內政部「創意狂想,巢向未來」智慧化居住空間創意競賽巢向未來組(案場實作組)金獎 [專案挑戰] 為提供多段控制作為切換依據,本案估計之佔比須為連續數值(否則一旦控制模式數量改變就須重新訓練),然而人工標記連續數值的標籤困難,僅能提供粗略標記(尖峰/離峰);此外,為顧及部署門檻,本系統需要能在非常有限的標籤數量下建置。 [專案設計] 為應對上述挑戰,本案核心設計如下: (1) 使用離散標籤學會輸出連續數值的方法:本設計將問題轉化為:若在數線上0代表離峰的感測器狀態,而1代表尖峰感測器狀態,則該如何根據感測器狀態間關係把居中樣本平滑的映射在0到1之間。受WGAN的Discriminator設計的啟發,本案以類似的方式對網路加上Lipschitz Continuous的限制來達到此目的。 (2) 稀疏標籤問題:為避免模型因標籤稀疏而導致模型收斂至違背物理現象的解,本案將各種感測器對於空間人員多寡的相關性(如:人在辦公室會開啟電腦,故用電量和人員多寡大至呈正相關)編寫為梯度限制的損失函數,藉此增加模型的先驗假設。
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趙梓吟
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Published: May 30th 2022
34
3
0
Tools
Python
Deep learning
Semi-supervised learning
Occupancy estimation
WGAN
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