Integrating Pearson Correlation and Hybrid Models for Renewable Energy Demand Forecasting in Turkey

Achieving carbon neutrality, enhancing energy efficiency, securing energy supply, and accurately forecasting energy demand are among the most urgent global energy priorities. In this study, Turkey’s geothermal, wind, and solar electricity consumption was forecasted for the 2025–2030 period using five years of historical data through eight different regression-based models. The forecast models included ARIMA, Linear Regression, Polynomial Regression, Exponential Smoothing, Ridge, Lasso, SVR, and XGBoost. Forecast accuracy was validated using 2023–2024 data. A hybrid model, integrating the Lasso and Random Forest approaches via weighted averaging, was developed to enhance forecast robustness. Pearson correlation was applied to quantify the impact of key socioeconomic variables—such as population, GDP, and university graduates—on energy consumption patterns. Forecast comparisons revealed that Random Forest and XGBoost produced results closest to the Hybrid model, with deviation rates of 1.84–7.27% and 0.03–1.08%, respectively. In contrast, Polynomial Regression and Exponential Smoothing showed significant biases, with deviations reaching up to 61.58% and 54.48% in 2030. ARIMA remained relatively consistent but exhibited increasing deviation over time. The Exponential and Polynomial models consistently overestimated demand, while SVR underestimated it throughout the forecast horizon. Ridge Regression provided stable but systematically higher forecasts. The findings indicate that the hybrid model provides a balanced forecasting structure and mitigates the under- or overestimation tendencies observed in singular models. This research supports strategic, data-driven energy planning in alignment with long-term sustainability goals.