Advancing nuclear energy forecasting: Exploring regression modeling techniques for improved accuracy - Anjali Nighoskar; Preeti Chaurasia; Nagendra Singh (Nuclear Engineering and Technology)
The urgent requirement for sustainable and dependable energy sources has stimulated an increased fascination with precisely forecasting nuclear energy generation. This work utilizes sophisticated regression modeling approaches, namely XGBoost, to predict nuclear energy generation by leveraging economic indices such as Gross Domestic Product (GDP). Each model's prediction accuracy has been evaluated by examining historical data on nuclear energy output and GDP from various locations. Here, measures such as mean squared error (MSE) and coefficient of determination (R2) to analyze their effectiveness have been used. The results of this study demonstrate that the XGBoost model outperforms standard regression approaches, showing greater R2 values and lower MSE scores. Furthermore, the consequences of these discoveries for the development of energy policy offer possible directions for future study in energy forecasting. This study provides useful insights for energy planners and policymakers, enabling a more profound comprehension of the complex relationship between economic indicators and nuclear energy generation.