EStack Fusion Model For Energy Demand Forecasting

Precise forecasting of energy demand is important for good energy management and grid stability at different times. Energy demand is subject to the effects of both temperature and humidity along with other seasonal or periodic influences, making the forecasting task sufficiently complicated. This study presents the EStack Fusion Model: an ensemble method of XGBoost, Gradient Boosting, and Long Short-Term Memory (LSTM) networks in order to improve forecasting accuracy. For prediction, the model uses key features in terms of energy demand, temperature, humidity, holiday status, and month. Data preprocessing procedures, such as normalization and missing value treatment, have been implemented to ensure data quality. The dataset was split into training and testing sets, and various measures such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) for evaluating the model's performance were used. EStack Fusion outperforms individual models, with a 5.7411 MSE, 1.3601 MAE, and 0.8391 R2 showing a 1.45% improvement from the best standalone model. Proposed ensemble method captures non-linear dependencies and temporal variations. It is suitable for real-world problems such as smart grid management, demand response systems, and renewable energy forecasting. However, computational efficiency and scalability for real-time deployment remain crucial research issues. Future research will focus on optimal techniques, lightweight architectures, and adaptive learning models to further enhance prediction accuracy and efficiency.