Analysis of heating day degrees in Turkey withmachine learning techniques

This study examined the feasibility of utilizing Artificial Neural Networks (ANN), a machine learning technique, to analyze Turkey's Heating Degree Day (HDD) data and the possible advantages of such analyses. A literature review was initially performed on the conventional methods employed for calculating HDD values. The efficacy of machine learning techniques was evaluated to gain knowledge about heating demands throughout Turkey's various temperature zones. The research examined the usefulness of the ANN methodology for this objective. The appropriateness of employing these techniques in HDD analysis and their potential integration into modeling were examined comprehensively. We analyzed the potential enhancements in HDD forecast accuracy resulting from this integration. The literature review indicated that machine learning techniques can offer analytical insights for accurately determining heating requirements across various geographical areas in Turkey. The application of these methodologies in this context can significantly enhance energy efficiency studies and facilitate the optimization of energy management systems. Keywords: Machine Learning, Heating Day Degrees, Energy Efficiency, Model Analysis