( 您好!臺灣時間:2021/03/06 20:00
字體大小: 字級放大   字級縮小   預設字形  


研究生(外文):Jung-Hsing Chen
論文名稱(外文):Duration Forecast for Construction Engineering Subject to the Impact of Wind Uncertainty: An Study of Wind Turbine Construction Project
外文關鍵詞:Duration ForecastImpact of Wind UncertaintyWind Turbine ConstructionSimulationFuzzy Theory
  • 被引用被引用:0
  • 點閱點閱:258
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
為確保風力發電機組興建工程能在規劃期程內完成並開始啟用供電,在台灣,目前大多數的風力發電機組興建工程合約大都採用日曆天計算工期。惟規劃這些日曆天時並未仔細考量風不確定因素影響所產生之風險,目前工程界實務上對於受天候影響之工期預估大多是根據經驗法則以概估方式處理,惟此概估方式常因人而異,若風不確定性因素之影響超乎預期,則工期延誤之風險相應而增加。因此,承包商需承受更多的風險。好的專案管理(project management)係要減少不確定因素所造成之不良影響,而使專案時程能依循所規畫之排程進行;同時,亦能動態地調整所規畫之排程俾利符合專案實際執行情況。因此,如何準備一個受到風的負面衝擊之風險最小的規畫排程實為一風力發電機組興建工程中重要的課題。
Due to many concerns of global climate changing, many countries are making efforts for reducing carbon emission and increasing green energy in order to develop a low carbon and sustainable society. Because of its characteristics of cleanness, inexhaustibility, low pollution and low cost, wind energy becomes one of the most promising renewable energy sources as an alternative to conventional fossil, coal, or nuclear sources of energy. Therefore, many countries are increasingly devoted to establish wind farms with multi-megawatt sized wind turbines for sustainable green energy developments. Ritter et al. (2015) noted that the global cumulative installed capacity of wind energy increased from 6 GW in 1996 to 318 GW in 2013 and is expected to reach 596 GW in 2018. Thus, it is foreseeable that a large number of wind turbine construction projects will be carried out worldwide in the near future.
Taiwan has few indigenous energy resources. More than 98% of energy resource is imported from overseas, mainly conventional fossil, coal, or nuclear sources of energy. How to develop self-produced energy instead of depending on the importation to ensure steady supply is one of the major concerns for future energy development in Taiwan. With abundant wind energy potential existed, Taiwan’s government has been actively promoting installation of wind farms for the reduction of carbon emissions and the development of sustainable energy.
Wind turbines mainly capture wind energy to produce electricity; therefore, the sites selected for constructing wind farm, particularly onshore or offshore, are definitely with plentiful of strong wind. However, strong wind could lead to significant productivity loss of some wind-sensitive tasks resulting in adversely affecting the scheduling of wind turbine construction and causing delay. Consequently, the wind, simultaneously playing both positive and negative roles in the wind farm construction project, makes schedule control a challenging job. Accordingly, wind is a significant weather risk for productivity loss and schedule delay in wind turbine construction projects.
In Taiwan, most wind turbine construction projects are now awarded on a calendar-day basis, in which the contract period has been specified without considering the wind impact in each calendar day. As results, the contractors will take more schedule risks than ever before. Unfortunately, conventional scheduling techniques (e.g., critical path method) are not able to catch the impact of wind uncertainties. In practice, the impact of wind uncertainties is usually estimated by a rule of thumb based on engineers’ past experiences and experts’ subjective judgements and is roughly adjusted by adding an estimated amount of time. However, different experiences and judgements from various professionals will result in different adjustments. Also, inappropriately adjustment could not keep the project on the track of the stipulated schedule plan, which results in raising serious events of disputes or penalty between the contractor and the owner. Simulation is an effective approach to handle the uncertainties involved in various aspects of construction management, such as scheduling and productivity estimation. Therefore, scientifically simulating various scenarios based on historical wind speed data in plan/design phase would more accurately evaluate the impact of wind uncertainties to minimize the duration risks of wind turbine construction project.
With the assistance of professional expertise, the incorporation of fuzzy-set approach, as well as the utilization of historical wind speed data, this research presents a duration estimation model for wind turbine construction project subject to the impact of wind uncertainties,. An appropriate duration for each wind-sensitive task based on no-wind condition can be assigned based on a rule of thumb; afterwards, the simulated duration subject to the impact of wind uncertainties can be derived by the proposed model. An application example using this model to estimate the duration of wind-sensitive tasks is presented using actual wind speed data from Taiwan. This model is easier to follow and simpler to apply. By employing this approach, the duration can be more accurately estimated even though without much relevant working experience provided.
目 錄
口試委員審定書 I
誌 謝 II
研究摘要 IV
目 錄 IX
圖目錄 XI
表目錄 XIII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與限制 4
1.4 研究方法及流程 5
第二章 文獻回顧 7
2.1 全球風力發電產業發展現況及趨勢 7
2.2 國內風力發電產業發展現況及趨勢 12
2.3 風的分級與風速的轉換 15
2.4 模糊理論及隸屬函數 19
2.5 模糊理論於排程之應用 23
第三章 分析模式及計算依據 25
3.1 判別受風敏感之作業項目 25
3.2 各作業項目受不同風力影響之生產力折減參數 26
3.3 隸屬函數 33
3.4 生產力損失 36
3.5 計算準則 37
3.6 排程模擬系統 40
第四章 案例研析 45
4.1 案例基本資料 45
4.2 氣象站基本資料 56
4.3 模擬結果 57
4.4 模擬結果研析 63
第五章 結論與建議 69
5.1 結論 69
5.2 研究貢獻 70
5.3 建議 72
參考文獻 73
附 錄 79
1.Apipattanavis, S., Sabol, K., Molenaar, K.R., Rajagopalan, B., Xi, Y., Blackard, B., and Patil, S., “Integrated framework for quantifying and predicting weather-related highway construction delays,” Journal of Construction Engineering and Management, ASCE, Vol. 136, No. 11, pp. 1160-1168 (2010).
2.Ayyub, B.M., and Halder, A., “Project scheduling using fuzzy set concepts,” Journal of Construction Engineering and Management, ASCE, Vol. 110, No. 2, pp. 189-204 (1984).
3.Bataineh, K. M., and Dalalah, D., “Assessment of wind energy potential for selected areas in Jordan,” Renewable Energy, Vol. 59, pp. 75-81 (2013).
4.Borowy, B.S., and Salameh, Z.M., “Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system,” IEEE Transactions on Energy Conversion, Vol. 11, No. 2, pp. 367-375 (1996).
5.Bhandari, B., Lee, K.T., Lee, G.Y., Cho, Y.M., and Ahn, S.H., “Optimization of hybrid renewable energy power system: A review,” International Journal of Precision Engineering and Manufacturing Green Technology, Vol. 2, No. 1, pp. 99-112 (2015).
6.Carr, R.I. “Simulation of construction project duration,” Journal of Construction Division, ASCE, Vol. 105, No. 2, pp. 117-128 (1979)
7.Chang, C.T., and Lee, H.C., “Taiwan’s renewable energy strategy and energy-intensive industrial policy,” Renewable and Sustainable Energy Reviews, Vol. 64, pp. 456-465 (2016)
8.Chao, L.C., and Hsiao C.S., “Fuzzy model for predicting project performance based on procurement experiences,” Automation in Construction, Vol. 28, pp. 71-81 (2012).
9.El-Rayes, K., and Moselhi, O., “Impact of rainfall on the productivity of highway construction,” Journal of Construction Engineering and Management, ASCE, Vol. 127, No. 2, pp. 125-131 (2001).
10.Guo, S.J., “Computer-aided project duration forecasting subjected to the impact of rain,” Journal of Computer-Aided Civil and Infrastructure Engineering, Vol.15, No. 1, pp. 67-74 (2000).
11.Gupta, R.A., Kumar, R., and Bansal, A.K., “BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting,” Renewable and Sustainable Energy Reviews, Vol. 41, pp. 1366-1375 (2015).
12.Jeong, H.S., Atreya, S., Oberlender, G.D., and Chung, B.Y., “Automated contract time determination system for highway projects,” Automation in Construction, Vol. 18, No. 7, pp. 957-965 (2009).
13.Justus, C.G., and Mikhail, A., “Height variation of wind speed and wind distributions statistics,” Geophysical Research Letters, Vol. 3, No. 5, pp. 261-264 (1976).
14.Katinas, V., Sankauskas, D., Markevicius, A., and Perednis, E., “Investigation of the wind energy characteristics and power generation in Lithuania,” Renewable Energy, Vol. 66, pp. 299-304 (2014).
15.Khamooshi, H., and Golafshani, H., “EDM: Earned duration management, a new approach to schedule performance management and measurement,” International Journal of Project Management, Vol. 32, No. 6, pp. 1019-1041 (2014).
16.Koehn, E., and Brown, G., “Climatic effects on construction,” Journal of Construction Engineering and Management, ASCE, Vol. 111, No. 2, pp. 129-137 (1985).
17.Korman, R., Setzer, S.W., and Powers, M.B., “Rains wreck summer schedules,” Engineering News Record, McGraw-Hill Construction Weekly, August 31 (1992), 6-7.
18.Lorterapong, P., and Moselhi, O., “Project-network analysis using fuzzy sets theory,” Journal of Construction Engineering and Management, ASCE, Vol. 122, No. 4, pp. 308-318 (1996).
19.Mendel, J.M., “Fuzzy logic systems for engineering: a tutorial,” Proceedings of the IEEE, Vol. 83, No. 3, pp.345-377 (1995).
20.Mirahadi, F., and Zayed, T., “Simulation-based construction productivity forecast using neural-network-driven fuzzy reasoning,” Automation in Construction, Vol. 65, pp.102-115 (2016).
21.Moder, J.J., Phillips, C.R., Davis, E.W., “Project management with CPM, PERT and Precedence Diagramming,” Van Norstrand Reinhold, New York, pp. 270-311 (1983)
22.Mohandes, M., Rehman, S., and Rahman, S.M., “Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS),” Applied Energy, Vol. 88, No. 11, pp. 4024-4032 (2011).
23.Moselhi, O., Gong, D., and El-Rayes, K., “Estimating weather impact on the duration of construction activities,” Canadian Journal of Civil Engineering, Vol. 24, No. 3, pp. 359-366 (1997).
24.Padilla, E.M., and Carr, R.I., “Resource strategies for dynamic project management,” Journal of Construction Engineering and Management, ASCE, Vol.117, No. 2, pp. 279-293 (1991).
25.Pan, N.F., “Assessment of productivity and duration of highway construction activities subject to impact of rain,” Expert Systems with Applications, Vol. 28, No. 2, pp. 313-326 (2005).
26.Quan, P., and Leephakpreeda, T., “Assessment of wind energy potential for selecting wind turbine: An application to Thailand,” Sustainable Energy Technologies and Assessments, Vol. 11, pp.17-26 (2015).
27.Ritter, M., Shen, Z., Cabrera, B.L., and Odening, M., “Designing an index for assessing wind energy potential,” Renewable Energy, Vol. 83, pp. 416-424 (2015).
28.Salameh, Z.M., and Safari, I., “Optimum windmill-site matching,” IEEE Transactions on Energy Conversion, Vol. 7, No. 4, pp. 669-676 (1992).
29.Smith, G.R., and Hancher, D.E., “Estimating precipitation impacts for scheduling,” Journal of Construction Engineering and Management, ASCE, Vol. 115, No. 4, pp. 552-566 (1989).
30.Thomas, H.R., and Yiakoumis, I., “Factor model of construction productivity,” Journal of Construction Engineering and Management, ASCE, Vol. 113, No. 4, pp. 623-639 (1987).
31.Touran, A., and Wiser, E.P., “Monte Carlo technique with correlated random variables,” Journal of Construction Engineering and Management, ASCE, Vol. 118, No. 2, pp.258-272 (1992)
32.Zadeh, L.A., “Fuzzy sets,” Information and Control, Vol. 8, No. 3, pp. 338-353 (1965).
33.“Global Wind Report 2015 – annual market update GWEC
34.“Manual on Marine Meteorological Services - Volume 1: Global Aspects,” 2012 edition, World Meteorological Organization 2012, (WMO –No. 558),
51.Danish Wind Industry Association,http://www.windpower.org/
52.American Wind Energy Association,http://www.awea.org/
53.European Wind Energy Association,http://www.ewea.org/
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔