臺灣博碩士論文加值系統

電能是我們生活中不可或缺之能源，其中以冷氣空調耗電量佔整體耗電量之大宗。而為達到全球推動之節約能源政策，安裝智慧電表不僅能監測電器耗電量，亦可利用本文研擬的分析方法預測未來耗電量，提高智慧電表的使用價值。本研究由文獻回顧中得到啟發，以臺灣科技大學營建工程系之研究室為實驗空間，針對建築空間內空調耗能情形進行數據收集，結合環境監測、紅外線感測器和風扇調節系統，探討環境變因對於空調耗電量之影響，並建立耗電量預測模型。由於冷氣空調耗電量資料同時具有線性與非線性的特質，因此本研究提出一混合式模型，結合線性自回歸移動平均(ARIMA)模型以及非線性的萬用啟發式最小平方機器學習(MetaFA-LSSVR)模型推估冷氣空調耗電量，統合稱為自回歸移動平均結合萬用啟發式優化機器學習法(ARIMA- MetaFA-LSSVR)。此法將回歸運算分為兩部分，首先透過ARIMA模型訓練線性預測並計算殘差值，再連同第一部分產出的值和影響耗電量因子套入MetaFA-LSSVR模型中獲得瞬間耗電量預測值。運算過程中，結合滑動視窗法彌補大資料量處理效能的落差，茲以快速預測次日耗電量。而從敏感度分析中得出，整合ARIMA輸出值與考量時間(小時)與紅外線感測頻率屬性時，為此冷氣空調資料庫之最適模型，其每分鐘耗電量預測值與實際值平均單天預測誤差的相關係數(correlation coefficient, R)為62%、方根誤差(root mean square error, RMSE) 為0.41kw、絕對誤差(mean absolute error, MAE) 為0.19kw、總誤差率(total error, TER)為8%。評估結果顯示，本研究提出之自回歸啟發式優化機器學習模型可有效先期預測次日的冷氣空調每分鐘耗電量，茲以作為建物管理單位與使用者於空調設備節電規畫決策資訊，建構智慧建築遠端監測耗能及用電量趨勢預測系統之雛形。

Electricity is an essential form of energy in our lives. Of all the electricity consumed by people, that for air conditioners accounts for the highest proportion. In response to energy conservation policies advocated around the world, we recommend the installation of smart meters. Concerning smart meters, they not only monitor the electricity consumption of electrical appliances, but can also be combined with the analytical method developed in this study to predict future electricity consumption, thereby enhancing the usage value of smart meters. In this study, literature was reviewed to find inspirations and experiments were conducted inside a laboratory of the Department of Civil and Construction Engineering, the National Taiwan University of Science and Technology. Data were collected to identify the air conditioning usage situation in architectural space. Next, an environmental monitoring method, infrared sensors, and fan adjustment systems were used to investigate the effects of environmental variables on the electricity consumption situation of air conditioners as well as to build an electricity consumption forecast model. Because electricity consumption data of air conditioners display both linear and nonlinear characteristics, this study introduced a hybrid model, in which a linear autoregressive integrated moving average (ARIMA) model and a nonlinear nature-inspired metaheuristic-optimized least squares support vector regression (MetaFA–LSSVR) model were combined to estimate the electricity consumption of air conditioners; the hybrid model was called “ARIMA–MetaFA–LSSVR.” This model divided regression operation into two parts, in which the ARIMA model first trained the system to make linear predictions and calculated residual values. Next, values created from the previous step and factors that influenced electricity consumption were incorporated into the MetaFA–LSSVR model to generate forecast values. During the calculation procedure, moving windows were used to make up for poor data processing efficacy and predict electricity consumption for the following day. A sensitivity analysis showed that the optimal ARIMA–MetaFA–LSSVR model (for the air conditioner database used in this study) was obtained by using ARIMA (1,0,2) and considering the time (measured in h) and the infrared sensor frequencies. Concerning the comparison between energy consumption predictions and actual electricity consumptions, the said model was able to achieve a correlation coefficient (R) of 62%, a root mean square error (RMSE) of 0.41, a mean absolute error (MAE) of 0.19, and a total error (TER) of 4%. Therefore, the ARIMA–MetaFA–LSSVR introduced in this study can be used to predict the per-minute electricity consumption of air conditioners for the following day.

摘要
Abstract
致謝
目錄
表目錄
圖目錄
第一章 緒論
1.1. 研究背景與動機
1.2. 研究目的
1.3. 研究流程與論文架構
第二章 文獻回顧
2.1. 各國智慧電網發展及應用情形
2.2. 智慧電網的優勢與效益
2.3. 監測及預測耗電功率對於使用行為之影響
2.4. 人工智慧於耗電量之預測應用
第三章 研究方法
3.1. 耗電功率監測系統實驗設計
3.1.1. 監測設備儀器之限制
3.1.2. 分析因子選定
3.2. 自回歸移動平均結合萬用啟發式優化機器學習(ARIMA-MetaFA-LSSVR)模型
3.2.1. 自回歸移動平均(ARIMA)模型
3.2.2. 萬用啟發式優化機器學習(MetaFA-LSSVR)
3.2.3. ARIMA和MetaFA-LSSVR模型整合
3.3. 模型預測誤差評估方法
第四章 耗電功率監測系統開發與設計
4.1. 監測系統與設定
4.1.1. 耗電功率監測設備
4.1.2. 環境監測系統
4.1.3. 耗電功率與環境監測系統
4.2. 監測資訊網與資料庫
4.2.1. 監測資訊網
4.2.2. 量測數據接收介面
第五章 資料分析與模型成果評估
5.1. 數據預處理
5.2. 模型建立與敏感度分析
5.3. 評估成果與討論
第六章 結論與建議
參考文獻
附錄一 監測屬性表
附錄二 ARIMA-MetaFA-LSSVR模型原始程式碼
附錄三 MySQL資料庫與MATL AB模型操作
附錄四 電能消耗和影響因子的歷史資料
附錄五 試誤法進行敏感度分析測試MetaFA-LSSVR lags
附錄六 LSSVR模型原始程式碼

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