A hybrid simulation tool to improve the energy efficiency in production environment

Since energy efficiency has obtained much attention from researchers and this situation will last for several decades, various simulation tools were developed to describe and predict the energy-consumption behavior of both production processes and building facilities in order to optimize energy efficiency. Physically based modelling (PBM) to describe thermodynamic interaction between production activities and building facilities is one of most utilized solutions, which involves a huge amount of analytical efforts and causes some difficulties in practice. This research work aims to develop a hybrid simulation tool to quantify the heating/cooling requirement considering different production and weather scenarios. Combined with a data-driven model (DDM) through the application of machine learning (ML) and a PBM based on thermodynamic interactions, this tool achieves a good accuracy as well as a manageable modeling effort. A minimal heating/cooling effort can be derived through using thermal impacts from production and atmosphere. Additionally, a comparison of various production plans under the same condition shows up to 40 % difference of energy consumption. This finding indicates that an adjustment of production planning can lead to a further energy saving from heating/cooling.