Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

Abstract

Industrial machinery is a significant energy consumer, and its CO 2 emissions have increased dramatically in recent years. Therefore, energy efficiency is becoming crucial for businesses, governments, as well as the planet. Estimating the power consumption of industrial machines with greater accuracy assists management and optimizes machine operation parameters. Real-time industrial machine datasets present several challenges, such as changes in the data over time, unknown running conditions, missing data, etc. Most research publications focus on the accuracy of traditional static models of forecasting; however, prediction performance deteriorates over time because data evolves. We implemented deep learning as a prediction model for three distinct real-world industrial datasets. The proposed method, dynamic modeling with memory (DMWM), improved overall prediction performance compared with conventional approaches by identifying concept drifts and optimizing the number of required models in response to industrial datasets' recurring machine energy consumption patterns.

Short-term Load Forecasting and Regime Switching Detection Based on Normalized Causal Entropy Boosting

“…Since the load distribution can change abruptly or gradually over time, accurate load forecasting requires not only identifying load patterns but also maintaining long-term memory of historical observations. Detecting load regime switching and predicting load demand is important for facilitating optimal management of energy, improved grid stability, and reduction of energy costs and waste [4][5][6].…”