Integrating Pearson Correlation and Hybrid Models for Renewable Energy Demand Prediction in Turkey: 2025–2030 Case Study

Achieving carbon neutrality, enhancing energy efficiency, planning renewable energy installations, ensuring energy supply security, and accurately addressing energy demand are among the most critical global energy priorities. In this context, energy demand forecasting plays a pivotal role in shaping energy policies and ensuring systematic planning. Accurate demand-side forecasting contributes to improved energy efficiency and the formulation of long-term strategic energy policies. This study focuses on forecasting Turkey’s geothermal, wind, and solar energy consumption for the period 2025–2030 using five years of historical consumption data. A total of eight different regression-based forecasting models were employed. By validating model accuracy with 2023–2024 data, a hybrid prediction model was developed to generate reliable forecasts for 2025–2030. In the hybrid estimation model, the impact power of the variables affecting the formation of demand was calculated using the Pearson Correlation Statistical method. The results aim to support data-driven energy planning and policy development in alignment with sustainability goals.