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題名 利用專利分析進行技術預測：評估浮動式離岸風電技術創新發展
Technological Forecasting by Patent Analysis: The Development of Floating Offshore Wind Energy Technology作者 陳思妤
Chen, Sih-Yu貢獻者 柯玉佳
Ko, Yu-Chia
陳思妤
Chen, Sih-Yu關鍵詞 浮動式離岸風電
技術預測
成長曲線
專利分析
floating offshore wind energy
technology forecasting
growth curves
patent analysis日期 2023 上傳時間 1-Dec-2023 11:56:23 (UTC+8) 摘要 本研究利用專利數據以分析浮動式離岸風電技術進行，採用Logistic model和Gompertz model進行技術預測評估，藉由這兩種模型的分析，評估專利數據對於預測技術發展的適用性，並了解浮動式離岸風電技術的演進。 本研究首先使用Logistic model和Gompertz model進行技術預測，以評估兩模型對浮動式離岸風電技術領域的解釋能力，以確定何種模型更能準確預測該領域技術的發展趨勢。本研究將基於專利數據的相關指標，分析浮動式離岸風電領域的技術生命週期發展階段，藉此瞭解該技術的成長趨勢和關鍵發展階段，以提供政府或企業投入該領域的依據。本研究將探討專利分類號對浮動式離岸風電技術領域的分類，將有助於理解不同專利分類號在技術領域中的應用。 研究結果顯示在浮動式離岸風電領域中，Gompertz model在模型適配能力與預測能力皆能夠展現良好的表現績效。此外，浮動式離岸風電技術目前處於成長期中期皆段，仍存在快速成長的空間，因此專利申請量將持續性增加。最後，針對相關技術的開發，除了應用於浮動式離岸風電領域，未來亦將可能擴展至其他多種再生能源技術中，是能夠協助能源轉型與淨零排放目標的重要技術。
This study utilizes patent data to analyze the floating offshore wind energy technology. Logistic model and Gompertz model are applied for technology forecasting. Through these two models for analyzing, the study aims to comprehensively understand the domain of floating offshore wind energy technology through patent data analysis. By using the Logistic model and Gompertz model for technology forecasting to assess the performance of both models within the field of floating offshore wind energy technology. This assessment aims to select which model accurately predicts the development and prospect of this domain. Based on patent data indicators, this study explores the various stages of the technological lifecycle in the floating offshore wind energy domain. This exploration observes the growth trends and the development stages of this technology, and providing a reference for the government or the corporate. The study also explores the patent classification codes of the categorization of floating offshore wind energy technology, and offering insights for the applications of different patent classification codes. The result shows Gompertz model can fit the data of the floating offshore wind energy well. Furthermore, the technology of floating offshore wind energy currently reaches the middle of the growth stage. Consequently, patent applications are expected to increase continuously. Lastly, in addition to its application in the floating offshore wind energy domain, the development of the key technologies might extend to other renewable energy technologies. This expansion has the potential to promote the energy transition and make a significant contribution to achieving emissions reduction objective.參考文獻 中文文獻 朱文伶（2010），行動電話擴散研究之模型選用及驅動因子分析，國立政治大學科技管理研究所博士論文，台北，取自https://hdl.handle.net/11296/b5j8r6。 吳欣穎（2009），科技產品生命週期之預測模型比較，國立交通大學管理科學系博士論文，新竹，取自https://hdl.handle.net/11296/4vmy9g。 吳彥儒（2023），展望國際浮動式離岸風電發展趨勢與我國策略布局，臺灣經濟研究月刊，Vol. 46，Pages 32-40。 袁建中（2005），產業分析之技術預測方法與實例，麥格羅‧希爾出版社。 張登凱（2021），透過專利數據進行技術預測：探討自駕車技術之擴散，國立政治大學科技管理與智慧財產研究所碩士論文，台北，取自https://hdl.handle.net/11296/m94537。 陳達仁、黃慕萱（2018），專利資訊檢索、分析與策略，華泰文化事業股份有限公司。 經濟部智慧財產局（2020），離岸風電專利分析報告，取自https://pcm.tipo.gov.tw/PCM2010/PCM/commercial/03/WindPower.aspx?aType=3&Articletype=1&aSn=767。 Tacx（2022），離岸風力發電場建構工程操作概論（二版），東美出版事業有限公司。 外文文獻 Adamuthe, A. C., & Thampi, G. T. (2019). Technology forecasting: A case study of computational technologies. Technological forecasting and social change, 143, 181-189. Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based on patent data. Technological forecasting and social change, 96, 202-214. Campani, M., & Vaglio, R. (2015). A simple interpretation of the growth of scientific/technological research impact leading to hype-type evolution curves. Scientometrics, 103(1), 75-83. Campbell, R. S. (1983). Patent trends as a technological forecasting tool. World Patent Information, 5(3), 137-143. Chanchetti, L. F., Diaz, S. M. O., Milanez, D. H., Leiva, D. R., de Faria, L. I. L., & Ishikawa, T. T. (2016). Technological forecasting of hydrogen storage materials using patent indicators. International Journal of Hydrogen Energy, 41(41), 18301-18310. Chen, Y.-H., Chen, C.-Y., & Lee, S.-C. (2011). Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies. International Journal of Hydrogen Energy, 36(12), 6957-6969. Cheng, A.-C., & Chen, C.-Y. (2008). The technology forecasting of new materials: the example of nanosized ceramic powders. Romanian Journal of Economic Forecasting, 4, 88-110. Chu, W.-L., Wu, F.-S., Kao, K.-S., & Yen, D. C. (2009). Diffusion of mobile telephony: An empirical study in Taiwan. Telecommunications policy, 33(9), 506-520. Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological forecasting and social change, 73(8), 981-1012. Díaz, H., Serna, J., Nieto, J., & Guedes Soares, C. (2022). Market needs, opportunities and barriers for the floating wind industry. Journal of Marine Science and Engineering, 10(7), 934. Dubarić, E., Giannoccaro, D., Bengtsson, R., & Ackermann, T. (2011). Patent data as indicators of wind power technology development. World Patent Information, 33(2), 144-149. Ernst, H. (1997). The use of patent data for technological forecasting: the diffusion of CNC-technology in the machine tool industry. Small business economics, 9, 361-381. Foster, R. N. (1986). Working the S-curve: assessing technological threats. Research Management, 29(4), 17-20. Gao, L., Porter, A. L., Wang, J., Fang, S., Zhang, X., Ma, T., Wang, W., & Huang, L. (2013). Technology life cycle analysis method based on patent documents. Technological forecasting and social change, 80(3), 398-407. Griliches, Z. (1998). Patent statistics as economic indicators: a survey. In R&D and productivity: the econometric evidence (pp. 287-343). University of Chicago Press. Group, T. F. A. M. W. (2004). Technology futures analysis: Toward integration of the field and new methods. Technological forecasting and social change, 71(3), 287-303. GWEC. (2021). GLOBAL WIND REPORT 2021. GWEC. (2023). Global Wind Report 2023. https://gwec.net/globalwindreport2023/ Ho, J. C., Saw, E.-C., Lu, L. Y., & Liu, J. S. (2014). Technological barriers and research trends in fuel cell technologies: A citation network analysis. Technological forecasting and social change, 82, 66-79. Huang, Y., Li, R., Zou, F., Jiang, L., Porter, A. L., & Zhang, L. (2022). Technology life cycle analysis: From the dynamic perspective of patent citation networks. Technological forecasting and social change, 181, 121760. IPO. (2021). A worldwide overview of offshore wind power patents. https://www.gov.uk/government/publications/a-worldwide-overview-of-offshore-wind-power-patents IRENA. (2016). Floating foundations: A game changer for offshore wind. James, R., & Ros, M. C. (2015). Floating offshore wind: market and technology review. The Carbon Trust, 439. Johnstone, N., Haščič, I., & Popp, D. (2010). Renewable energy policies and technological innovation: evidence based on patent counts. Environmental and resource economics, 45, 133-155. Joo, K., Lee, M., & Lee, G. (2022). Technology originality and convergence analysis in the wind power field using patents. Energies, 15(9), 3316. Kaewtapee, C., Khetchaturat, C., & Bunchasak, C. (2011). Comparison of growth models between artificial neural networks and nonlinear regression analysis in Cherry Valley ducks. Journal of Applied Poultry Research, 20(4), 421-428. Kapoor, R., Karvonen, M., Ranaei, S., & Kässi, T. (2015). Patent portfolios of European wind industry: New insights using citation categories. World Patent Information, 41, 4-10. Karvonen, M., Lehtovaara, M., Kapoor, R., Kassi, T., & Pyrhonen, J. (2012). Analyzing the emerging offshore wind power market technologies. 2012 Proceedings of PICMET'12: Technology Management for Emerging Technologies, Lee, S., Marcu, M., & Lee, S. (2011). An empirical analysis of fixed and mobile broadband diffusion. Information economics and policy, 23(3-4), 227-233. Lin, D., Liu, W., Guo, Y., & Meyer, M. (2021). Using technological entropy to identify technology life cycle. Journal of Informetrics, 15(2), 101137. Lindman, Å., & Söderholm, P. (2016). Wind energy and green economy in Europe: measuring policy-induced innovation using patent data. Applied energy, 179, 1351-1359. Liu, S.-J., & Shyu, J. (1997). Strategic planning for technology development with patent analysis. International journal of technology management, 13(5-6), 661-680. Martin, C. A., & Witt, S. F. (1989). Forecasting tourism demand: A comparison of the accuracy of several quantitative methods. International Journal of Forecasting, 5(1), 7-19. Martino, J. P. (1993). Technological forecasting. The Futurist, 27(4), 13. Meade, N. (1984). The use of growth curves in forecasting market development—a review and appraisal. Journal of Forecasting, 3(4), 429-451. Meade, N., & Islam, T. (1995). Forecasting with growth curves: An empirical comparison. International Journal of Forecasting, 11(2), 199-215. Meade, N., & Islam, T. (1998). Technological forecasting—Model selection, model stability, and combining models. Management science, 44(8), 1115-1130. Meade, N., & Islam, T. (2015). Forecasting in telecommunications and ICT—A review. International Journal of Forecasting, 31(4), 1105-1126. Meyer, P. S., & Ausubel, J. H. (1999). Carrying capacity: a model with logistically varying limits. Technological forecasting and social change, 61(3), 209-214. Mueller, S. C., Sandner, P. G., & Welpe, I. M. (2015). Monitoring innovation in electrochemical energy storage technologies: A patent-based approach. Applied energy, 137, 537-544. Park, C., Lim, S., Shin, J., & Lee, C.-Y. (2022). How much hydrogen should be supplied in the transportation market? Focusing on hydrogen fuel cell vehicle demand in South Korea: Hydrogen demand and fuel cell vehicles in South Korea. Technological forecasting and social change, 181, 121750. Porter, A. L. (1991). Forecasting and management of technology (Vol. 18). John Wiley & Sons. Porter, A. L., & Rossini, F. (1987). Technological forecasting. Encyclopedia of System and Control, 4823-4828. Sossa, J. W. Z., Marro, F. P., Alzate, B. A., Salazar, F. M. V., & Patiño, A. F. A. (2016). S-Curve analysis and technology life cycle. Application in series of data of articles and patents. Revista ESPACIOS| Vol. 37 (Nº 07) Año 2016. Stuart, T. E., & Podolny, J. M. (1996). Local search and the evolution of technological capabilities. Strategic management journal, 17(S1), 21-38. Trappey, C. V., & Wu, H.-Y. (2008). An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles. Advanced Engineering Informatics, 22(4), 421-430. Trappey, C. V., Wu, H.-Y., Taghaboni-Dutta, F., & Trappey, A. J. (2011). Using patent data for technology forecasting: China RFID patent analysis. Advanced Engineering Informatics, 25(1), 53-64. TRUST, C. (2015). Floating offshore wind market technology review. TRUST, C. (2022). FLOATING WIND JOINT INDUSTRY PROGRAMME Phase IV Summary Report. https://ctprodstorageaccountp.blob.core.windows.net/prod-drupal-files/documents/resource/public/FLW_P4_Summaryreport.pdf Tsai, Y.-C., Huang, Y.-F., & Yang, J.-T. (2016). Strategies for the development of offshore wind technology for far-east countries–a point of view from patent analysis. Renewable and Sustainable Energy Reviews, 60, 182-194. WFO. (2023). Global Offshore Wind Report 2022. https://wfo-global.org/reports/ Wright, J. T., & Giovinazzo, R. A. (2000). Delphi-uma ferramenta de apoio ao planejamento prospectivo. Caderno de pesquisas em administração, 1(12), 54-65. Yang, X., Yu, X., & Liu, X. (2018). Obtaining a sustainable competitive advantage from patent information: A patent analysis of the graphene industry. Sustainability, 10(12), 4800. Young, P. (1993). Technological growth curves: a competition of forecasting models. Technological forecasting and social change, 44(4), 375-389. Zhu, H., Du, Z., Wu, J., & Sun, Z. (2022). Innovation environment and opportunities of offshore wind turbine foundations: Insights from a new patent analysis approach. World Patent Information, 68, 102092. 描述 碩士
國立政治大學
科技管理與智慧財產研究所
110364125資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110364125 資料類型 thesis dc.contributor.advisor 柯玉佳 zh_TW dc.contributor.advisor Ko, Yu-Chia en_US dc.contributor.author (Authors) 陳思妤 zh_TW dc.contributor.author (Authors) Chen, Sih-Yu en_US dc.creator (作者) 陳思妤 zh_TW dc.creator (作者) Chen, Sih-Yu en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Dec-2023 11:56:23 (UTC+8) - dc.date.available 1-Dec-2023 11:56:23 (UTC+8) - dc.date.issued (上傳時間) 1-Dec-2023 11:56:23 (UTC+8) - dc.identifier (Other Identifiers) G0110364125 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/148524 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 科技管理與智慧財產研究所 zh_TW dc.description (描述) 110364125 zh_TW dc.description.abstract (摘要) 本研究利用專利數據以分析浮動式離岸風電技術進行，採用Logistic model和Gompertz model進行技術預測評估，藉由這兩種模型的分析，評估專利數據對於預測技術發展的適用性，並了解浮動式離岸風電技術的演進。 本研究首先使用Logistic model和Gompertz model進行技術預測，以評估兩模型對浮動式離岸風電技術領域的解釋能力，以確定何種模型更能準確預測該領域技術的發展趨勢。本研究將基於專利數據的相關指標，分析浮動式離岸風電領域的技術生命週期發展階段，藉此瞭解該技術的成長趨勢和關鍵發展階段，以提供政府或企業投入該領域的依據。本研究將探討專利分類號對浮動式離岸風電技術領域的分類，將有助於理解不同專利分類號在技術領域中的應用。 研究結果顯示在浮動式離岸風電領域中，Gompertz model在模型適配能力與預測能力皆能夠展現良好的表現績效。此外，浮動式離岸風電技術目前處於成長期中期皆段，仍存在快速成長的空間，因此專利申請量將持續性增加。最後，針對相關技術的開發，除了應用於浮動式離岸風電領域，未來亦將可能擴展至其他多種再生能源技術中，是能夠協助能源轉型與淨零排放目標的重要技術。 zh_TW dc.description.abstract (摘要) This study utilizes patent data to analyze the floating offshore wind energy technology. Logistic model and Gompertz model are applied for technology forecasting. Through these two models for analyzing, the study aims to comprehensively understand the domain of floating offshore wind energy technology through patent data analysis. By using the Logistic model and Gompertz model for technology forecasting to assess the performance of both models within the field of floating offshore wind energy technology. This assessment aims to select which model accurately predicts the development and prospect of this domain. Based on patent data indicators, this study explores the various stages of the technological lifecycle in the floating offshore wind energy domain. This exploration observes the growth trends and the development stages of this technology, and providing a reference for the government or the corporate. The study also explores the patent classification codes of the categorization of floating offshore wind energy technology, and offering insights for the applications of different patent classification codes. The result shows Gompertz model can fit the data of the floating offshore wind energy well. Furthermore, the technology of floating offshore wind energy currently reaches the middle of the growth stage. Consequently, patent applications are expected to increase continuously. Lastly, in addition to its application in the floating offshore wind energy domain, the development of the key technologies might extend to other renewable energy technologies. This expansion has the potential to promote the energy transition and make a significant contribution to achieving emissions reduction objective. en_US dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機 3 第三節 研究目的與問題 4 第四節 研究方法與流程 5 第二章 文獻回顧 6 第一節 浮動式離岸風電 6 第二節 技術預測 24 第三節 技術生命週期 28 第四節 專利資訊與專利分析 37 第五節 再生能源技術之技術預測相關實證研究 42 第六節 研究缺口與小節 45 第三章 研究方法 47 第一節 研究結構 47 第二節 數據蒐集 48 第三節 專利申請數趨勢分析 57 第四節 模型參數估計 58 第五節 模型解釋能力評估 60 第四章 研究結果與分析 63 第一節 專利件數趨勢分析 63 第二節 技術預測模型分析 64 第三節 研究發現與討論 70 第四節 技術發展趨勢與應用 77 第五章 結論與建議 80 第一節 研究結論 80 第二節 研究貢獻 82 第三節 研究限制與建議 83 參考文獻 85 zh_TW dc.format.extent 2837604 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110364125 en_US dc.subject (關鍵詞) 浮動式離岸風電 zh_TW dc.subject (關鍵詞) 技術預測 zh_TW dc.subject (關鍵詞) 成長曲線 zh_TW dc.subject (關鍵詞) 專利分析 zh_TW dc.subject (關鍵詞) floating offshore wind energy en_US dc.subject (關鍵詞) technology forecasting en_US dc.subject (關鍵詞) growth curves en_US dc.subject (關鍵詞) patent analysis en_US dc.title (題名) 利用專利分析進行技術預測：評估浮動式離岸風電技術創新發展 zh_TW dc.title (題名) Technological Forecasting by Patent Analysis: The Development of Floating Offshore Wind Energy Technology en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文文獻 朱文伶（2010），行動電話擴散研究之模型選用及驅動因子分析，國立政治大學科技管理研究所博士論文，台北，取自https://hdl.handle.net/11296/b5j8r6。 吳欣穎（2009），科技產品生命週期之預測模型比較，國立交通大學管理科學系博士論文，新竹，取自https://hdl.handle.net/11296/4vmy9g。 吳彥儒（2023），展望國際浮動式離岸風電發展趨勢與我國策略布局，臺灣經濟研究月刊，Vol. 46，Pages 32-40。 袁建中（2005），產業分析之技術預測方法與實例，麥格羅‧希爾出版社。 張登凱（2021），透過專利數據進行技術預測：探討自駕車技術之擴散，國立政治大學科技管理與智慧財產研究所碩士論文，台北，取自https://hdl.handle.net/11296/m94537。 陳達仁、黃慕萱（2018），專利資訊檢索、分析與策略，華泰文化事業股份有限公司。 經濟部智慧財產局（2020），離岸風電專利分析報告，取自https://pcm.tipo.gov.tw/PCM2010/PCM/commercial/03/WindPower.aspx?aType=3&Articletype=1&aSn=767。 Tacx（2022），離岸風力發電場建構工程操作概論（二版），東美出版事業有限公司。 外文文獻 Adamuthe, A. C., & Thampi, G. T. (2019). Technology forecasting: A case study of computational technologies. Technological forecasting and social change, 143, 181-189. Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based on patent data. Technological forecasting and social change, 96, 202-214. Campani, M., & Vaglio, R. (2015). A simple interpretation of the growth of scientific/technological research impact leading to hype-type evolution curves. Scientometrics, 103(1), 75-83. Campbell, R. S. (1983). Patent trends as a technological forecasting tool. World Patent Information, 5(3), 137-143. Chanchetti, L. F., Diaz, S. M. O., Milanez, D. H., Leiva, D. R., de Faria, L. I. L., & Ishikawa, T. T. (2016). Technological forecasting of hydrogen storage materials using patent indicators. International Journal of Hydrogen Energy, 41(41), 18301-18310. Chen, Y.-H., Chen, C.-Y., & Lee, S.-C. (2011). Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies. International Journal of Hydrogen Energy, 36(12), 6957-6969. Cheng, A.-C., & Chen, C.-Y. (2008). The technology forecasting of new materials: the example of nanosized ceramic powders. Romanian Journal of Economic Forecasting, 4, 88-110. Chu, W.-L., Wu, F.-S., Kao, K.-S., & Yen, D. C. (2009). Diffusion of mobile telephony: An empirical study in Taiwan. Telecommunications policy, 33(9), 506-520. Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological forecasting and social change, 73(8), 981-1012. Díaz, H., Serna, J., Nieto, J., & Guedes Soares, C. (2022). Market needs, opportunities and barriers for the floating wind industry. Journal of Marine Science and Engineering, 10(7), 934. Dubarić, E., Giannoccaro, D., Bengtsson, R., & Ackermann, T. (2011). Patent data as indicators of wind power technology development. World Patent Information, 33(2), 144-149. Ernst, H. (1997). The use of patent data for technological forecasting: the diffusion of CNC-technology in the machine tool industry. Small business economics, 9, 361-381. Foster, R. N. (1986). Working the S-curve: assessing technological threats. Research Management, 29(4), 17-20. Gao, L., Porter, A. L., Wang, J., Fang, S., Zhang, X., Ma, T., Wang, W., & Huang, L. (2013). Technology life cycle analysis method based on patent documents. Technological forecasting and social change, 80(3), 398-407. Griliches, Z. (1998). Patent statistics as economic indicators: a survey. In R&D and productivity: the econometric evidence (pp. 287-343). University of Chicago Press. Group, T. F. A. M. W. (2004). Technology futures analysis: Toward integration of the field and new methods. Technological forecasting and social change, 71(3), 287-303. GWEC. (2021). GLOBAL WIND REPORT 2021. GWEC. (2023). Global Wind Report 2023. https://gwec.net/globalwindreport2023/ Ho, J. C., Saw, E.-C., Lu, L. Y., & Liu, J. S. (2014). Technological barriers and research trends in fuel cell technologies: A citation network analysis. Technological forecasting and social change, 82, 66-79. Huang, Y., Li, R., Zou, F., Jiang, L., Porter, A. L., & Zhang, L. (2022). Technology life cycle analysis: From the dynamic perspective of patent citation networks. Technological forecasting and social change, 181, 121760. IPO. (2021). A worldwide overview of offshore wind power patents. https://www.gov.uk/government/publications/a-worldwide-overview-of-offshore-wind-power-patents IRENA. (2016). Floating foundations: A game changer for offshore wind. James, R., & Ros, M. C. (2015). Floating offshore wind: market and technology review. The Carbon Trust, 439. Johnstone, N., Haščič, I., & Popp, D. (2010). Renewable energy policies and technological innovation: evidence based on patent counts. Environmental and resource economics, 45, 133-155. Joo, K., Lee, M., & Lee, G. (2022). Technology originality and convergence analysis in the wind power field using patents. Energies, 15(9), 3316. Kaewtapee, C., Khetchaturat, C., & Bunchasak, C. (2011). Comparison of growth models between artificial neural networks and nonlinear regression analysis in Cherry Valley ducks. Journal of Applied Poultry Research, 20(4), 421-428. Kapoor, R., Karvonen, M., Ranaei, S., & Kässi, T. (2015). Patent portfolios of European wind industry: New insights using citation categories. World Patent Information, 41, 4-10. Karvonen, M., Lehtovaara, M., Kapoor, R., Kassi, T., & Pyrhonen, J. (2012). Analyzing the emerging offshore wind power market technologies. 2012 Proceedings of PICMET'12: Technology Management for Emerging Technologies, Lee, S., Marcu, M., & Lee, S. (2011). An empirical analysis of fixed and mobile broadband diffusion. Information economics and policy, 23(3-4), 227-233. Lin, D., Liu, W., Guo, Y., & Meyer, M. (2021). Using technological entropy to identify technology life cycle. Journal of Informetrics, 15(2), 101137. Lindman, Å., & Söderholm, P. (2016). Wind energy and green economy in Europe: measuring policy-induced innovation using patent data. Applied energy, 179, 1351-1359. Liu, S.-J., & Shyu, J. (1997). Strategic planning for technology development with patent analysis. International journal of technology management, 13(5-6), 661-680. Martin, C. A., & Witt, S. F. (1989). Forecasting tourism demand: A comparison of the accuracy of several quantitative methods. International Journal of Forecasting, 5(1), 7-19. Martino, J. P. (1993). Technological forecasting. The Futurist, 27(4), 13. Meade, N. (1984). The use of growth curves in forecasting market development—a review and appraisal. Journal of Forecasting, 3(4), 429-451. Meade, N., & Islam, T. (1995). Forecasting with growth curves: An empirical comparison. International Journal of Forecasting, 11(2), 199-215. Meade, N., & Islam, T. (1998). Technological forecasting—Model selection, model stability, and combining models. Management science, 44(8), 1115-1130. Meade, N., & Islam, T. (2015). Forecasting in telecommunications and ICT—A review. International Journal of Forecasting, 31(4), 1105-1126. Meyer, P. S., & Ausubel, J. H. (1999). Carrying capacity: a model with logistically varying limits. Technological forecasting and social change, 61(3), 209-214. Mueller, S. C., Sandner, P. G., & Welpe, I. M. (2015). Monitoring innovation in electrochemical energy storage technologies: A patent-based approach. Applied energy, 137, 537-544. Park, C., Lim, S., Shin, J., & Lee, C.-Y. (2022). How much hydrogen should be supplied in the transportation market? Focusing on hydrogen fuel cell vehicle demand in South Korea: Hydrogen demand and fuel cell vehicles in South Korea. Technological forecasting and social change, 181, 121750. Porter, A. L. (1991). Forecasting and management of technology (Vol. 18). John Wiley & Sons. Porter, A. L., & Rossini, F. (1987). Technological forecasting. Encyclopedia of System and Control, 4823-4828. Sossa, J. W. Z., Marro, F. P., Alzate, B. A., Salazar, F. M. V., & Patiño, A. F. A. (2016). S-Curve analysis and technology life cycle. Application in series of data of articles and patents. Revista ESPACIOS| Vol. 37 (Nº 07) Año 2016. Stuart, T. E., & Podolny, J. M. (1996). Local search and the evolution of technological capabilities. Strategic management journal, 17(S1), 21-38. Trappey, C. V., & Wu, H.-Y. (2008). An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles. Advanced Engineering Informatics, 22(4), 421-430. Trappey, C. V., Wu, H.-Y., Taghaboni-Dutta, F., & Trappey, A. J. (2011). Using patent data for technology forecasting: China RFID patent analysis. Advanced Engineering Informatics, 25(1), 53-64. TRUST, C. (2015). Floating offshore wind market technology review. TRUST, C. (2022). FLOATING WIND JOINT INDUSTRY PROGRAMME Phase IV Summary Report. https://ctprodstorageaccountp.blob.core.windows.net/prod-drupal-files/documents/resource/public/FLW_P4_Summaryreport.pdf Tsai, Y.-C., Huang, Y.-F., & Yang, J.-T. (2016). Strategies for the development of offshore wind technology for far-east countries–a point of view from patent analysis. Renewable and Sustainable Energy Reviews, 60, 182-194. WFO. (2023). Global Offshore Wind Report 2022. https://wfo-global.org/reports/ Wright, J. T., & Giovinazzo, R. A. (2000). Delphi-uma ferramenta de apoio ao planejamento prospectivo. Caderno de pesquisas em administração, 1(12), 54-65. Yang, X., Yu, X., & Liu, X. (2018). Obtaining a sustainable competitive advantage from patent information: A patent analysis of the graphene industry. Sustainability, 10(12), 4800. Young, P. (1993). Technological growth curves: a competition of forecasting models. Technological forecasting and social change, 44(4), 375-389. Zhu, H., Du, Z., Wu, J., & Sun, Z. (2022). Innovation environment and opportunities of offshore wind turbine foundations: Insights from a new patent analysis approach. World Patent Information, 68, 102092. zh_TW
