Time Series Forecasting Models for Energy Load Prediction

Abstract

Accurate energy load prediction is crucial for effective energy management and planning in modern power systems. Time series forecasting models have emerged as powerful tools for predicting future energy consumption based on historical data patterns. This study explores various time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA), Seasonal Decomposition of Time Series (STL), and machine learning approaches like Long Short-Term Memory (LSTM) networks. By analyzing historical energy load data, the study assesses the performance of these models in terms of prediction accuracy, computational efficiency, and adaptability to seasonal variations.

The research methodology involves a comprehensive evaluation of each model's ability to capture trends, seasonality, and cyclic patterns inherent in energy consumption data. Using datasets from diverse geographic regions, we perform a comparative analysis of model performance, employing metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The findings reveal that hybrid models combining traditional time series methods with machine learning algorithms significantly enhance prediction accuracy, providing more reliable forecasts for energy load.

The implications of this research extend to energy providers and policymakers, offering insights into optimizing energy distribution, reducing costs, and ensuring grid stability. By improving the accuracy of energy load predictions, this study contributes to the advancement of smart grid technologies and supports sustainable energy management practices. The results underscore the importance of adopting sophisticated forecasting models to address the evolving demands of energy consumption in an increasingly dynamic environment