# A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
## 4 Solar Technology Patent Review and Analysis
### Cross-Comparison analysis  
The technology development between patents and academic literature is quite different. As previously stated, most patents describe solar hydropower storage systems with a variety of subsystems relevant to indirect solar collection technology. Academic literature most frequently proposes new frameworks or algorithms for grid-connected electricity supply systems. The proposed systems use novel light absorbing materials for photovoltaic panels to lower the cost of the system and improve manufacturability. New system frameworks must be carefully planned for precise implementation and integration since new approaches require complex examination processes and other various factors such as social and government acceptance. Systems are relatively easy to implement if they are improvements based on existing systems. Conversely, it is very difficult to implement innovative systems and algorithms as a first attempt. Therefore, it is reasonable that there are more novel technologies describing integration of renewable energy generation systems and simulation of grid-connected energy storage systems in the literature, while technologies describing solar hydropower storage system are presented in patents. The doc2 vec model is trained using all the patent files in this research domain, so the user can put any group of files within a specific technology domain to generate a list of the most relevant patents. The number of output patents can also be determined by the user. The input data for Case 1 contains 107 academic articles selected from the key field literature dataset. The target field covers Topic 1 under Cluster 3 between 2016 and 2018, which is the largest topic group from the largest cluster. The 10 output patents having the highest similarity with the input data are shown in Table 4. The output patents are evenly distributed over the years 2010 to 2018, indicating the technology has developed constantly. The content of the patents focuses on the application of PV solar cells such as mobile power supply systems, mosquito killing devices, greenhouse roof heating systems and immune identification systems. For Case 2, the integration of smart grid and intelligent electricity management systems are reviewed. The search was for novel technologies combining solar power systems (including power generation, storage and supply systems) with cyber-physics systems and big data generation. The input data contained 24 articles. The input literature is selected from Cluster 2 of the literature dataset which describes the simulation of grid-connected energy storage systems and DC electricity supply. In order to obtain the latest technologies, the target literature is published in the year 2018. By training the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Patent Number** & **Year** & **Title** \\ \hline CN201590776U & 2010 & Mobile photovoltaic power supply system. \\ \hline CN201983524U & 2011 & Multi-energy complementary ground source heat pump system based on independent power generation. \\ \hline CN202519366U & 2012 & Pipeline for silicon wafers diffusion of polycrystalline silicon PV cells. \\ \hline CN103461299A & 2013 & Solar power mosquito killing device. \\ \hline CN104032982A & 2014 & Heating and cooling system of power generation for solar greenhouse roof. \\ \hline CN204762528U & 2015 & Zero energy consumption constant temperature greenhouse lighting system. \\ \hline CN102496650B & 2015 & Manufacturing method for solar cell module. \\ \hline CN107374775A & 2017 & PV intelligent identification for immune system. \\ \hline CN104032982B & 2018 & Heating and cooling system of power generation for solar greenhouse roof. \\ \hline CN206838588U & 2018 & PV panel cleaning device. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Doc2 vec output for Case 1.  
doc2 vec model, Table 5 shows 10 output patents which are most similar to the input documents. The 10 recommended patents are quite new (all published after 2013) compared to the whole patent dataset (published from 2008 to 2018).