Deep Learning and Text-Embedding to Integrate Energy Consumption into Industrial Machine Production Planning

In modern industrial manufacturing, simulation and accurate forecasting of energy consumption are critical to optimise resources and reduce waste meeting the significant energy demands and greenhouse gas emissions of this sector. Industry 5.0 (15.0), focused on sustainability and human-centered manufacturing, emphasises the use of Artificial Intelligence (AI), the Industrial Internet of Things (1IoT) and the Cyber Physical Systems (CPS) to monitor and optimise resource use in real time. This paper proposes a novel energy consumption simulation method using Neural Network (NN), specifically the Deep Echo State Network (DeepESN), integrated with a text embedding model, which allows to combine energy consumption data into production scheduling. Unlike existing approaches, our method considers both machine-level energy consumption and production workflows, enabling comprehensive optimisation of energy effi-ciency. Preliminary tests in a real production scenario show the potential of the approach, highlighting its ability to predict energy consumption simply by using smart meters without additional hardware. This work represents a significant advancement in in-tegrating energy consumption modeling into production planning and contributes to more sustainable industrial practices.