Data-driven Approach for Discovery of Energy Saving Potentials in Manufacturing Factory

Over the life cycle of a manufacturing factory, continuous improvement in energy efficiency plays a critical role in reducing environmental impacts. Driven by regulations and cost pressure, more and more manufacturing companies have implemented energy conservation technologies and systems to improve their energy efficiency as an important part for continuous improvement of their sustainable performance. However, the more successful a company achieves in energy efficiency improvement, the more challenging it becomes for the company to identify areas for further improvement. This paper proposes an approach and methods to discover energy saving potentials by analyzing the dynamics, complexity, and interrelated energy consumption data of machines or factory. The approach consists of 4 major steps: data preparation, correlation analysis, energy efficiency frontier analysis, and energy saving potential quantification. Further extension to machine-learning is discussed for possible control of machines running parameters to consistently operate at close to best achieved energy efficiency.