Analysis of SARIMA Models for Forecasting Electricity Demand

This article presents an in-depth evaluation of electricity consumption predictions using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Leveraging historical electricity consumption data, the SARIMA model demonstrates a commendable ability to forecast future consumption patterns. Our analysis reveals a strong alignment between the model's predictions and actual consumption data, affirming the efficacy of time series modeling in capturing complex energy consumption dynamics. Notably, while the model excels in predicting near-term consumption trends, uncertainties widen for long-term forecasts, prompting critical reflections on the model's evolving accuracy and reliability over time. For future research endeavors, we recommend comparing the performance of diverse time series models to discern optimal modeling approaches. Further optimization of model parameters stands as a paramount endeavor to refine prediction accuracy and mitigate uncertainties. Specifically, efforts to identify and address potential overfitting or underfitting tendencies within the model are advised. Additionally, leveraging supplementary data sources and integrating seasonal factors could bolster the reliability of future predictions, expanding the model's predictive scope and ensuring more robust and precise forecasts.