Automatic Virtual Metrology for Long-term Energy Baseline Forecasting

Global warming poses significant environmental and economic challenges, with energy consumption being a major contributor. Industry 4.2 for Green Intelligent Manufacturing (I4.2-GiM) integrates digitalization, intelligent manufacturing, and energy management systems (EMSs) to optimize energy efficiency and achieve net zero. Accurate long-term energy forecasting is crucial but challenging due to complex consumption patterns and external influences. Automatic Virtual Metrology (AVM) enhances real-time quality prediction, with the second generation AVM (AVM<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</inf>) incorporating Convolutional Neural Networks (CNNs) for improving prediction accuracy. However, CNNs struggle with long-term dependencies. This study proposes the third generation AVM (AVM<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">III</inf>) by integrating Long Short-Term Memory (LSTM) networks to enhance long-term time-series predictions so as to enable smarter energy management in factories.