台灣在夏季尖峰用電時段，耗費在建築空調的用電往往佔總體發電量的三分之一，但大部份的空調負載容量卻往往因為未能精確低計算而做保守性的高估，使得空調主機經常處於低效率的半載狀態，不但浪費設備成本，同時也浪費電能。另外雖然較準確的空調負荷計算軟體已經容易取得，但由於其往往過於繁複，一般使用者無法正確使用。
　　為了使一般設計者能簡易又準確的估算空調負荷，本研究採用具公信力的大型空調軟體DOE2.2程式，以台北市十小時類辦公大樓依其特性及本地建材與氣象資料建立出準確的負荷來當作學習資料，然後採用倒傳遞類神經網路來學習這些資料，在經測試後可完成以類神經網路架構的簡易預測程式。為了更確定學習資料的準確性，也利用DOE程式計算一建築模型之冷負荷，結果語McQUISTON等人利用RTS法所估算的結果相當接近。
　　從測試結果顯示，完成學習的類神經網路容易使用，計算時間甚短，比起原DOE軟體預測誤差大約6.67%。

　　With a hot and humid weather in Taiwan, the consumption of electricity foe Air-Conditioning purpose is tremendously large and often exceed one third of summer peak load. Without an accurate estimation process, the majority of chillers used in the construction building is over-size fur to conservative load calculation. Many cooling facilities are always operating under a partial condition with low overall efficiency. Although, the large computer software which can accurately calculate the building cooling load is available and can be easily obtained through many channels, there are usually too complicate and difficult to be correctly applied.
　　In order to assist the designer to correctly estimate the cooling load in a simpler way, DOE code is used to calculate the cooling load for many office buildings located in Taipei. The results were used as the learning data for a neural network model. After a series of learning and testing processes, the revised network model is finalized and a simpler neural network program is completed in this study. It is worth mentioned that cooling load predicted by DOE code for an example building were very similar to the McQuiston results which were calculated by RTS method.
　　The simplified code developed in this study has an easy input procedure, very short computation time and a reasonable prediction accuracy. Comparing the predictions by the original DOE code, the prediction error of the current code is under 6.67%

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