# A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
## 5 Conclusions  
In this study, the academic literature and patents are reviewed to construct the knowledge domain ontology for solar energy and the derived subcategories for PV solar cells and concentrating solar thermal power generation technology (CSP). The ontology defines the relationships between the existing technologies of solar energy. By analyzing the statistical data and using text mining, the key development fields are discovered. The statistical data provides critical information attributes including top assignees, the top IPCs and the publishing year. The values are consolidated so that the technology R&D evolution trends can be tracked. For text mining, both patents and academic literature are collected to define the current research and development in terms of solar energy generation. The comparison between academic and industry R&D strategies are compared by text mining the results of both datasets. For text mining, there are four machine learning approaches, i.e., clustering, topic modeling, doc2 vec and patent evolution graphs, used for deriving analytical results. Clustering is used to group both academic literature papers and the patent documents into clusters. The trend for each patent and literature cluster is shown in Figures 10 and 11. Extracting keywords is an important process of this research. Without proper and precise keywords, the documents will not be properly assigned to the corresponding clusters. Therefore, normalized term frequency-inverse document frequency (NTF-IDF) is used to identify the key terms and avoid the document length problem. Further, the \"silhouette value\" is calculated to examine the cluster performance for the ideal number of clusters for any given document set. The technologies are divided into seven groups and topic models are generated under each cluster, for both patents and research articles. Using the key word distribution within each technology cluster, key clustered technologies are defined. For example, a user interested in the most mature technology can select the largest cluster with the most patents. In this cluster, the user is able to see the patent publishing trends and understand the document content in the cluster by checking the key words ranking and IPC ranking. To classify the documents in more detail, the user can refer to the topic model output within clusters. After the key domain technologies are discovered, results can be mapped to the ontology to identify research development opportunities. Using the word2 vec and doc2 vec approaches, this research retrieves or recommends the best related patents or literature that match the target (input) documents. For the patent evolution graph using MFCA, the target documents are plotted in concentric circles by years and connected based on their conceptual similarity. The result depicts the evolution of key technologies, which also cross-reference to the cluster(s) and topic(s) of best related literature. This research helps enterprises easily discover existing technologies related to solar energy-oriented research. The proposed machine learning approaches and technology mining process flow are generic, which can be applied to reviews and analyses of other technology domains. To summarize the contribution of this research, the readers can better understand the detailed technologies under each category by the construction of a machine learning program system and knowledge ontology. The proposed methodology framework can be referenced for further exploration of other technical aspects of solar technology. For instance, the recommendation system based on doc2 vec can be applied to describe the novel research or patents for the solar materials used on the panel surfaces. The results help energy companies review and select technologies related to their key technical strengths and R&D interests. For future work, this research can be extended by further combination of machine learning or deep learning approaches to explore the application and development of other types of renewable energy technologies.