IoT-enabled real-time energy efficiency optimisation method for energy-intensive manufacturing enterprises

Abstract

Rising energy prices, increasing fierce competition, new environmental legislation and concerns over climate change are forcing energy-intensive manufacturing enterprises to increase production energy efficiency and reduce their associated environmental impacts. Thanks to the rapid developments of technologies in Internet of Things (IoT), the real-time status of resources and the data of energy consumption from manufacturing processes can be collected easily. These manufacturing information can provide an opportunity to enhance the energy efficiency in real-time production management. To achieve this target, this work presents a real-time energy efficiency optimisation method (REEOM) for energy-intensive manufacturing enterprises. By this method, IoT technologies are applied to sense the real-time primitive production data, including the energy consumption data and the resources status data. Multilevel event model and complex event processing are used to obtain real-time energy-related key performance indicators (e-KPIs) which extend production performance indicators to the energy efficiency area. Then, the non-dominant sorting genetic algorithm II is used to schedule or reschedule the production plan in an energy-efficient way based on real-time e-KPIs. Finally, a case is used to demonstrate the presented REEOM.

Keywords:

• energy efficiency
• Internet of Things (IoT)
• complex event processing (CEP)
• energy optimisation

Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant Numbers 51675441 and U1501248], the Fundamental Research Funds for the Central Universities [Grant Number 3102017jc04001] and the 111 Project Grant of NPU [Grant Number B13044].

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant Numbers 51675441 and U1501248], the Fundamental Research Funds for the Central Universities [Grant Number 3102017jc04001] and the 111 Project Grant of NPU [Grant Number B13044].