## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.3 Words Analysis  
The analysis of keywords used in scientific articles provides an efficient way to investigate the language and content of scientific documents, yielding valuable insights in the field of research. This analysis can offer significant information about trends and characteristics within a specific domain or subdomain. The study of keywords can assist in understanding key concepts and the terminology employed in the field, proving helpful when clarification or definition of specific terms is needed. Furthermore, by examining groups of keywords that frequently appear together in documents, thematic clusters or groups of terms often associated with each other can be identified. This can provide an overview of subdomains or main themes within a field. Additionally, keywords associated with a high number of citations may indicate topics of great relevance and impact in the scientific community. This can aid in identifying works and subjects that have had a significant impact in the field. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Paper (First Author, Year, Journal, Reference)** & **Number of Authors** & **Region** & **Total Citations (TC)** & **Total Citations per Year (TC)** & **Normalized TC (NTC)** \\ \hline
1 & [PERSON] (2013), Ecosystem Services [66] & 4 & Denver, USA & 349 & 31.73 & 8.19 \\
2 & [PERSON] (2004), Mathematics and Computers in Simulation [26] & 2 & Australia & 130 & 6.5 & 1 \\
3 & [PERSON] 2000, Environment and Planning: A-Economy and Space [67] & 2 & London, England & 112 & 4.67 & 1 \\
4 & [PERSON] (2008), Transportation Research Part C-Emerging Technologies [68] & 1 & Belgrade, Serbia & 112 & 7 & 3.97 \\ [PERSON] (2018), Journal of Economic Dynamics \& Control [69] & 3 & Pisa, Italy & 103 & 17.17 & 4.59 \\ [PERSON] (2010), Expert Systems with Applications [70] & 3 & Shreveport, USA & 84 & 6 & 2.63 \\
7 & [PERSON] (2015), Value in Health [71] & 9 & Calgary, Canada & 82 & 9.11 & 2.67 \\
8 & [PERSON] (2006), International Journal of Geographical Information Science [72] & 2 & Southampton, England & 71 & 3.94 & 1.37 \\ [PERSON] (2018), IEEE Transactions on Smart Grid [73] & 4 & Bozeman, Montana & 70 & 11.67 & 3.12 \\ [PERSON] (2007), Philosophical Transactions of the Royal Society B-Biological Sciences [74] & 3 & Avon, England & 63 & 3.71 & 1.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top 10 most globally cited documents. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. ** & **Paper (First Author, Year, Journal, Reference)** & **Title** & **Main Elements** & **Purpose** & **Qualitative Analysis** \\ \hline  & & & & This paper explores a day-ahead retail electrical energy market with video-based demand response from an conditioning load using a hierarchical multiagent framework and machine learning. The retitle aims to maximize profit by setting optimal rules, while \(\alpha\)C agents optimize consumption patterns based on temperature set points. The retitle on the effectiveness of the retitle on machine learning to model aggregate load proposed architecture in behavior to adapt to data privacy constraints. Simulation results demonstrate simultaneous optimization of costs and increasing trailer growth behavior, leading to reduced power consumption costs and increased traffic profit. \\  & & & & proposed architecture is also effective in reducing peak loads, offering potential for determining/avoiding distribution system upgrades in high photovoltaic. \\  & & & & \\ \hline  & &  
Table 8 presents the top 10 most frequently occurring words in the \"keywords plus\" section of the analyzed documents. We can observe that the term \"simulation\" appears most frequently, suggesting a prevalent focus on simulated scenarios, experiments, or models in the research. The term \"model\" is also highly recurrent, indicating a substantial emphasis on the creation and analysis of various models within the documents.