Long Short-Term Memory Autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting

Abstract

Electricity consumption prediction is crucial for the operation, strategic planning, and maintenance of power grid infrastructure. The effective management of power systems depends on accurately predicting electricity usage patterns and intensity. This study aims to enhance the operational efficiency of power systems and minimize environmental impact by predicting mid to long-term electricity consumption in industrial facilities, particularly in forging processes, and detecting anomalies in energy consumption. We propose an ensemble model combining Extreme Gradient Boosting (XGBoost) and a Long Short-Term Memory Autoencoder (LSTM-AE) to accurately forecast power consumption. This approach leverages the strengths of both models to improve prediction accuracy and responsiveness. The dataset includes power consumption data from forging processes in manufacturing plants, as well as system load and System Marginal Price data. During data preprocessing, Expectation Maximization Principal Component Analysis was applied to address missing values and select significant features, optimizing the model. The proposed method achieved a Mean Absolute Error of 0.020, a Mean Squared Error of 0.021, a Coefficient of Determination of 0.99, and a Symmetric Mean Absolute Percentage Error of 4.24, highlighting its superior predictive performance and low relative error. These findings underscore the model’s reliability and accuracy for integration into Energy Management Systems for real-time data processing and mid to long-term energy planning, facilitating sustainable energy use and informed decision making in industrial settings.