教室環境節能智能優化之研究

余冠亨；呂光欽；廖仁忠；廖國凱；吳武杰；王啟川

物聯網 ； PMV ； 空氣品質 ； 照度 ； 節能 ； 機器學習 ； 最佳化控制 ； Internet of things (IoT) ； predicted mean vote (PMV) ； air quality ； illuminance ； energy saving ； machine learning ； optimization control

冷凍空調&能源科技

119期（2020 / 01 / 10）

24 - 35

繁體中文;英文

本研究為建立一套室內環境光、溫、空氣品質、能耗優化調控系統，透過物聯網技術進行設備間的資料擷取、通訊以及控制，結合Deep Q-Learning與影像偵測處理環境節能優化與人數及人員分佈問題，以交通大學工程五館132教室為實驗場域，一節課五十分鐘為單位，比較一般上課情況與此系統運作下之優化效果。研究結果顯示，在維持環境熱舒適度與空氣品質上，與定溫25度比較平均節省能耗19%，平均CO_2濃度減少21.3%，與定溫26度比較平均節省能耗15%，平均CO_2濃度減少12.9%；在維持環境光照度標準上，與一般使用情形下（所有燈具皆開至100%級別）比較平均節能36.49%、與所有座位維持最低照度規範上比較（所有燈具皆開至90%級別）平均節能32.02%，合計環境熱舒適度、空氣品質、光照度平均節能20.8%，平均CO_2濃度減少18.7%。

This study investigates the performance of an optimal indoor environment in a campus classroom. The control system is able to regulate and balance the needs for illuminance, thermal comfort, air quality, and energy consumption. The experiment test was conducted at classroom 132 of Engineering Bldg. 5 in NCTU where the class time is 50 minutes. The test results indicate that, by maintaining thermal comfort and air quality, when comparing with setting temperature at 25 degrees, the average energy saving is 19%, the average CO_2 concentration is decreased by 21.3%. When comparing with setting temperature at 26 degrees, the average energy saving is 15%, the average CO2 concentration is decreased by 12.9%. To meet the demand of illuminance standard, when comparing with the general use case (all luminaries are set at 100% level), the average energy saving is 36.49%. When comparing with the minimum illuminance standard for all seats (all luminaries are set at 90% level), the average energy saving is 32.02%. Under the operation of the system, the total average energy saving and CO2 reduction are 20.8% and 17.1%, respectively.

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