隨著逐年增長的用電量，台灣的工業用電已經超過總用電的 55%。且因碳排
放為導致的氣候異常之主因，節能儼然成為目前備受各界重視的議題。而高科技
廠房中，汙水處理為相當重要的一環，其中又以汙水處理設備的曝氣鼓風機用電
量最高。因此，本文將先探討鼓風機在更換新的設備後之節能效益與其量測驗證，
再進一步探討新的鼓風機系統進行能源管理的效益，使電能使用得到最佳化。
在過去的節能案例中常常會看到整個節能改善過程不夠透明化，並且沒有一
個統一的驗證標準。大多數人只使用了節能改善前後的耗電量作為節能量依據，
並沒有考慮到新舊機操作狀態的差異，導致節能量評估不夠準確。因此，近年來
國內也紛紛引入國際量測和驗證協議(IPMVP)作為節能量測驗證的規範，IPMVP
也是國際上被廣泛使用的準則。本研究將會遵循 IPMVP 所制定的標準對汙水處
理廠的鼓風機曝氣系統進行節能量測驗證，探討能源績效也將分為三個部分，第
一部分是針對單一機台的舊式感應馬達搭配羅茨式鼓風機更換為搭載永磁同步
馬達的空氣渦輪鼓風機，遵循量測與驗證的標準，使用最小平方法建立能耗迴歸
模型並計算最終的節能效益。第二部分會使用之前量測的數據並將量測邊界擴大
至兩台或是整個系統的方式比較其中的節能效益。第三部分將會討論新的鼓風機
系統進行能源管理，使用電路圖的線性類比方式模擬鼓風機系統，並使用多種最
佳化算法(布穀鳥演算法、基因演算法、拉格朗日乘數法)計算出在指定風量下的
最佳耗能，最後與傳統的平均負載法比較，得到除了更換新機台以外，探討利用
操作獲取更多的節能量。

With the drastic increase in electricity consumption year by year, Taiwan's
industrial electricity consumption has exceeded 55% of the national total electricity
consumption. Therefore, energy saving is an urgent task that has received much
attention across all industries. In high-tech factories, sewage treatment is a very
important part, among all processes, the aeration blower of sewage treatment equipment
consumes the highest amount of electricity. Therefore, this article will discuss the
energy-saving benefits of the blower after replacing the new blower and investigate
energy management for the new blower system to optimize electricity consumption for
different usage requirement.
In the past, energy-saving evaluation was often done while the entire energysaving improvement process was not transparent enough and there was no unified
verification standard. Most people only look at the electrical power consumption before
and after the energy conservation measures as the basis for energy-saving evaluation.
This approach does not consider the difference between the operating load of the old
and new blowers, resulting in inaccurate energy saving numbers. Therefore, in recent
years, the International Performance Measurement and Verification Protocol (IPMVP)
has also been introduced and promoted in Taiwan as a standardized approach for
energy-saving measurement and verification. IPMVP is also a widely used protocol
internationally. This study will follow the guide lines set by IPMVP and conduct
energy-saving measurements and verification of the blower system in the sewage
treatment plant. The study will also be divided into three parts. The first part is to
replace the roots blower with an air turbine blower. Following the guide lines of
measurement and verification, a least square method is used to establish a regression
IV
model of energy consumption and to calculate energy-saving benefits for single
machine. The second part compares the energy-saving benefits by using previously
measured data and extending the measurement boundary to two units or the entire
system. The third part will investigate energy management on the new blower system
for different operation strategies. The blower system is simulated using a circuit
diagram analogy. Different optimization algorithms, such as Cuckoo search algorithm,
genetic algorithm, and Lagrange multiplier method, are used to find the optimal energy
consumption operation under a specified air volume. Finally, the optimized operation
result is compared with the traditional average load method. More energy saving can
be obtained in addition to the machine replacement by operation strategy

[1] 經濟部能源局，2022，能源統計年報(110.01 版)
[2] 經濟部工業局工業污染防治技術服務團，“活性污泥操作維護手冊”，1993。
[3] United States Environmental Protection Agency. " Evaluation of Energy Conservation Measures for Wastewater Treatment Facilities “. September 2010
[4] 台灣綜合研究院，2005，國內成立能源服務公司最佳組合模式可行性評估，台灣電力公司專案研究計畫。
[5] 財團法人台灣綠色生產基金會，2020，量測驗證專業人才訓練班。
[6] Hamrin, J., Vine, E., & Sharick, A. (2007). The Potential for Energy Savings Certificates (ESC) as a Major Tool in Greenhouse Gas Reduction Programs. Report for The Center for Resource Solutions. Retrieved October 15, 2007
[7] Efficiency Valuation Organization. International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings, Volume I; Technical Report; Efficiency Valuation Organization: Washington, DC, USA, 2012
[8] 經濟部能源局指導財團法人台灣綠色基金會，2015，節能量的量測指導文件。
[9] Yau, Y. H., and K. S. Lim. "Energy analysis of green office buildings in the
tropics—photovoltaic system." Energy and Buildings 126 (2016): 177-193.
[10] David, Maxmillan, and N. Y. Dahlan. "Development of visual basic based GUI for option a energy savings of IPMVP." 2014 IEEE 4th International Conference on System Engineering and Technology (ICSET). Vol. 4. IEEE, 2014.
[11] Walter, Travis, Phillip N. Price, and Michael D. Sohn. "Uncertainty estimation improves energy measurement and verification procedures." Applied Energy 130 (2014): 230-236
[12] Ke, Ming-Tsun, Chia-Hung Yeh, and Cheng-Jie Su. "Cloud computing platform for real-time measurement and verification of energy performance." Applied Energy 188 (2017): 497-507.
[13] Ginestet, Stéphane, and Dominique Marchio. "Retro and on-going commissioning tool applied to an existing building: Operability and results of IPMVP." Energy 35.4 (2010): 1717-1723.
[14] Gallagher, Colm V., et al. "The suitability of machine learning to minimise
uncertainty in the measurement and verification of energy savings." Energy and Buildings 158 (2018): 647-655.
[15] 黃仲翊. 外部輸入非線性自動迴歸模型應用於冰水主機耗能分析. Diss. 國立台北科技大學, 2017.
[16] Zabidi, Azlee, et al. "Non-Linear Autoregressive with Exogenous Input (Narx)
Chiller Plant Prediction Model." 2021 International Conference on Software
Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM). IEEE, 2021.
[17] Amiri, Shideh Shams, Mohammad Mottahedi, and Somayeh Asadi. "Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the US." Energy and Buildings 109 (2015): 209-216.
[18] 柯明村，壓縮空氣乾燥設備之量測與驗證方法研究，中華水電冷凍空調月刊 105 年 03 月
[19] 柯明村, 何皇智, and 林育群. "粒子族群演算方法應用於空調機房節能效益之驗證." 冷凍空調與能源科技雜誌 57 (2009): 79-86.
[20] Granderson, Jessica, and Phillip N. Price. "Development and application of a
statistical methodology to evaluate the predictive accuracy of building energy
baseline models." Energy 66 (2014): 981-990.
[21] Ruiz, Germán Ramos, and Carlos Fernández Bandera. "Validation of calibrated energy models: Common errors." Energies 10.10 (2017): 1587
[22] Afroz, Zakia, et al. "An inquiry into the capabilities of baseline building
energy modelling approaches to estimate energy savings." Energy and Buildings 244 (2021): 111054
[23] Heo, Yeonsook, Ruchi Choudhary, and G. A. Augenbroe. "Calibration of building energy models for retrofit analysis under uncertainty." Energy and Buildings 47 (2012): 550-560.
[24] dos Santos Coelho, Leandro, et al. "Optimal chiller loading for energy
conservation using a new differential cuckoo search approach." Energy 75 (2014):
237-243.
[25] 廖奎鈞. 以改良式微分演化演算法優化配置多冰機系統. Diss. da tong da xue ji xie gong cheng yan jiu suo, 2018
[26] Chang, Yung-Chung. "A novel energy conservation method—optimal chiller loading." Electric Power Systems Research 69.2-3 (2004): 221-226.
[27] Chang, Yung-Chung. "Genetic algorithm based optimal chiller loading for energy conservation." Applied Thermal Engineering 25.17-18 (2005): 2800-2815.
[28] Chang, Yung-Chung, Jui-Kun Lin, and Meng-Hsuan Chuang. "Optimal chiller loading by genetic algorithm for reducing energy consumption." Energy and Buildings 37.2 (2005): 147-155.
[29] dos Santos Coelho, Leandro, and Viviana Cocco Mariani. "Improved firefly algorithm approach applied to chiller loading for energy conservation." Energy and Buildings 59 (2013): 273-278.
[30] P. Newbold, W. L. Carlson and B. Thorne(2013). Statistics for Business and the Economics, 8?? Edition, Pearson.
[31] Abdi, Hervé. "The method of least squares." Encyclopedia of measurement and statistics 1 (2007): 530-532.
[32] Crassidis, John L., and John L. Junkins. Optimal estimation of dynamic systems. Chapman and Hall/CRC, 2004.
[33] Liao, J. G., and Dan McGee. "Adjusted coefficients of determination for logistic regression." The American Statistician 57.3 (2003): 161-165.
[34] Borutzky, Wolfgang. Bond graph modelling of engineering systems. Vol. 103. New York: Springer, 2011.
[35] Yang, Xin-She. Nature-inspired metaheuristic algorithms. Luniver press, 2010.
[36] Kaveh, Ali, and Taha Bakhshpoori. "Cuckoo search algorithm." Metaheuristics: Outlines, MATLAB Codes and Examples. Springer, Cham, 2019. 67-77.
[37] Katoch, Sourabh, Sumit Singh Chauhan, and Vijay Kumar. "A review on genetic algorithm: past, present, and future." Multimedia Tools and Applications 80.5 (2021): 8091-8126.
[38] Datta, Dilip, André RS Amaral, and José Rui Figueira. "Single row facility
layout problem using a permutation-based genetic algorithm." European Journal of Operational Research 213.2 (2011): 388-394.
[39] Lee, Carmen Kar Hang. "A review of applications of genetic algorithms in operations management." Engineering Applications of Artificial Intelligence 76 (2018): 1-12.
[40] Bertsekas, Dimitri P. Constrained optimization and Lagrange multiplier methods. Academic press, 2014.