{"title":"基于开放数据环境因素的机械通气数据驱动控制","authors":"H. Hsu, Chen-Yu Pan","doi":"10.1109/ICASI57738.2023.10179512","DOIUrl":null,"url":null,"abstract":"Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.","PeriodicalId":281254,"journal":{"name":"2023 9th International Conference on Applied System Innovation (ICASI)","volume":"606 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors\",\"authors\":\"H. Hsu, Chen-Yu Pan\",\"doi\":\"10.1109/ICASI57738.2023.10179512\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.\",\"PeriodicalId\":281254,\"journal\":{\"name\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"volume\":\"606 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASI57738.2023.10179512\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 9th International Conference on Applied System Innovation (ICASI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASI57738.2023.10179512","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors
Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.
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