## 3 Research Objectives and Methodology
### Description of Input Data and Data Sources  
The research is based on a detailed dataset covering several European countries with a high share of renewable energy sources (RES). The input data (Table 3) includes  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Applied Method** & **Objective of Analysis** & **Type of Output** & **Related Hypotheses** \\ \hline Descriptive Statistics & Quantification of basic price characteristics (mean, min, max, standard deviation) & Time series, volatility overview & H1, H2 \\ \hline Year-on-Year Volatility Analysis & Identification of shifts in price extremes and volatility across years & Comparative charts, tables & H2 \\ \hline Yearly Linear Regression & Modelling the relationship between daily RES production and electricity price per year & Regression equations, R\({}^{2}\) values & H3 \\ \hline Trend Quality Evaluation (R\({}^{2}\)) & Assessing inter-annual changes in explanatory power of simple trend models & Yearly R\({}^{2}\) comparison & H3 \\ \hline Correlation Matrix Analysis & Evaluating statistical relationships between production, residual load, and price & Annual and global correlation matrices & H1, H4 \\ \hline Negative Price Probability Analysis & Estimating the likelihood of negative prices based on RES generation levels & Frequency tables, threshold rules & H5 \\ \hline Rule-Based Pattern Extraction & Deriving interpretable conditions for high-risk pricing scenarios & Logical rules, practical recommendations & H5 \\ \hline Parameter-Based Data Filtering & Selection of specific timeframes, sources, or variables for sub-analysis & Exportable, filtered custom datasets & All \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of applied methods and their research objectives.  
time series of electricity prices (Day-Ahead Auction Prices) as well as generation values from individual sources (PV, wind, nuclear, and fossil). Complementary variables are environmental factors such as air temperature, wind speed, and the intensity of direct solar radiation, which have a direct impact on RES generation.  
The data have a high temporal resolution (in the range of 15 min to 1 h), which makes it possible to capture short-term fluctuations in prices and output. In particular, official sources such as national operating grids (e.g., ENTSO-E Transparency Platform), aggregated market portals, as well as publicly available datasets with environmental and meteorological data are used. The temporal scope of the data covers the last 8 to 10 years, with a particular focus on periods with a significant increase in the share of RES (e.g., 2022-2024). The data are preprocessed and structured in a uniform format, allowing dynamic analysis, filtering by months and source types, as well as aggregation by time levels (hourly, daily, monthly). Emphasis is placed on the representativeness of the input data for the countries of interest and consistency across variables.  
This data analysis is key to quantifying price volatility, modelling trends, analysing the occurrence of outliers (e.g., negative prices), and assessing relationships between sources. Given the structure of the data, it is possible to replicate the same methodology to other regions or to extend the analysis to other factors (e.g., energy imports/exports, support services, etc.).