太陽能係統及其相關技術已發展成為全球利用的綠色能源。對於提高太陽能轉換效率以提高太陽能在市場中的競爭力的新材料和新方法有許多創新研究。並且現有許多研究分析了太陽能領域的專利，發掘技術趨勢。但到目前為止還沒有同時針對專利和文獻進行交叉分析，其分別代表學術界及業界的發展狀況。在專利分析的文獻中，大多數研究僅使用單一方法或單一演算法進行文本探勘分析，如應用分群於專利文本分析。本研究提出了一種新的方法框架，通過應用多種機器學習之方法論來分析專利和文獻之資料集。該研究通過挖掘2280項專利和過去十年的5610篇文獻論文，欲挖掘其中有前景的太陽能技術。首先，從綜合文獻和專利文字探勘分析中構建了太陽能知識本體模式（或關鍵術語關係圖）。文字探勘分析則是利用分群的無監督機器學習技術，結合迪力克雷分布（LDA）之主題建模算法建構其主要主題。基於以上初步之機器學習演算法分析結果，本研究更進一步應用詞嵌入算法來建構技術推荐系統。該系統能雙向地找出專利及文獻兩個資料庫中含有最相關技術的文本，同時從其文本關鍵字能夠交互驗證太陽能特定子領域的前景技術。最後本研究透過專利演進圖來尋找技術演進過程。初步分析表明，許多專利都專注於太陽能水電存儲系統，以及將光能發電轉移到水力重力系統。本研究所建構之系統框架能有助於能源公司精準分析與其關鍵技術優勢和研發利益相關的技術，亦有助於學術界更有效率地提出有前景的技術發展。

Solar power systems and their related technologies have developed into a globally utilized green energy source. There are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. Also there are many existing studies analyzing patents in solar domain to discover the technology trend, but so far there is no reference conduct analysis to both patents and literatures, which reveal different aspect of development status. Moreover, most researches only use single methodology to do text mining. This, this research proposed a novel methodology framework by applying multiple machine learning measure to analyze both patent and literature dataset. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade. First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. unsupervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The results help energy companies select technologies related to their key technical strengths and R&D interests.

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