Data-driven Approach for Analyzing and Correlating Energy Market Products: Case Studies of Denmark and Croatia

Electricity price forecasting plays a vital role in the strategy decision making for almost all power market participants. This article investigates statistical background and potential relations between different power market products (e.g. day-ahead prices, intraday prices, etc.). Danish and Croatian power markets are used for the purpose of the case study to present the methods used in this article. First, Danish and Croatian power market structures are shortly explained to clarify the context of the problem. The data collection and preprocessing methods are described, followed by the core focus of the study: statistical analysis. In addition to the presented histograms of respective power market components, we examine interrelationships through statistical analysis, demonstrating significant correlations both numerically and graphically. Furthermore, price spreads are investigated as a logical next step of the noticed correlations. Our comparative analysis of Danish and Croatian market peculiarities reveals three key findings: ⅰ) statistically significant relationships between specific market components, ⅱ) distinct behavioral patterns among observed factors, and ⅲ) an open-access analytical tool with accompanying dataset for future research. Finally, the findings of this article present to market participants an efficient tool to adjust business strategies and increase profit.