## 4 Analysis and Interpretation of Results
### Regression Analysis of Price Trends
#### 4.5.1 Results of the Daily Regression Analysis  
The selection of variables in the regression analysis was based on both theoretical foundations and practical considerations reflecting the functioning of electricity markets. The main objective was to examine how the increasing share of renewable energy generation--specifically from solar and wind sources--affects the development of electricity prices. Therefore, in the simple linear regression model, the independent variable was the daily generation from renewables--specifically solar, wind, and combined solar + wind sources. This choice reflects the crucial role of these technologies in price formation due to their near-zero marginal costs, high variability, and limited predictability, all of which directly influence short-term price levels and volatility. The dependent variable was the average daily electricity price on the day-ahead market, which is highly sensitive to changes in renewable output and reflects their impact on market equilibrium. Each regression resulted in a regression equation expressing the trend and a coefficient of determination (R\({}^{2}\)), which indicates the extent to which price variability can be explained by the chosen type of RES generation. This approach allows for a quantitative comparison of the influence of different RES types on prices across countries and over time. In addition to assessing the quality of the models, special attention was paid to the sign of the regression line directive, which indicates whether there is a long-run increase or decrease in electricity prices in a given month over the period analysed. A positive directive with a sufficiently high R\({}^{2}\) would indicate a systematic price increase, while a negative directive would indicate a long-term downward trend in prices, potentially associated with an increased share of renewables and market dynamics. Given the structural changes in the electricity sector, including the 2022 energy crisis, the regression results were also interpreted in the context of contingencies that may have affected the linear trends. The results of the regression analysis (In Appendix A) show different trends in the average generation from solar and wind sources in the countries analysed over the period 2015-2025. In the case of Germany, very stable values of the regression guidelines close to zero were observed in the Solar category, with very low R\({}^{2}\) values (mostly below 0.1), which suggests the absence of a systematic trend in solar generation. In contrast, generation from wind and the sum of Solar + Wind showed slightly higher R\({}^{2}\) values, reaching as high as 0.4-0.5 in some years (e.g., combined Wind + Solar generation in 2025: R\({}^{2}\) = 0.518), indicating a moderate trend. This suggests that coordinated renewable generation is gradually increasing in Germany. In France, the trend lines were in most cases characterised by a slightly negative Solar directive and very low R\({}^{2}\) values, indicating weak and statistically insignificant trends. More significant trends were observed in the combined Wind + Solar category, where the R\({}^{2}\) value reached up to 0.437 in 2025, indicating a slightly systematic dynamics of production growth or decline. In the case of Poland, data for solar generation was not available for 2015-2019, but wind generation and combined solar/wind generation showed relatively moderate trends in later years. For example, the combined generation in 2023 had R\({}^{2}\) = 0.419, indicating a clear trend in the change in generation. This trend mainly reflects the increasing integration of wind farms in Poland during the last decade. Slovakia shows significantly weaker trends than other countries, with most models showing very low R\({}^{2}\) values (<0.1) and very small or fluctuating directives. The only exception is 2020, where a directive of \(-\)0.0075 and a relatively higher R\({}^{2}\) = 0.237 was identified in solar production, which may indicate a transient structural change in production or consumption behaviour.