Abstract
The ability to forecast electricity generation is crucial for effective energy management and policy planning. This study investigates the use of historical electricity generation data as a predictor for future generation trends using an autoregressive integrated moving average (ARIMA)-based regression model. Focusing on lagged values of electricity generation, we assess the predictive accuracy and statistical significance of the lagged variable (LAG1) for forecasting. The results indicate a strong positive relationship between past and future electricity generation, with the LAG1 coefficient being statistically significant at the 1% level. The regression model explains 97% of the variation in electricity generation, demonstrating its high utility for future forecasting. This analysis provides valuable insights for energy policymakers and stakeholders in preparing for future electricity demand.
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