A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques

Adequate energy awareness is an essential prerequisite for energy-efficient production planning and operation in manufacturing systems. Nonetheless, a lack of awareness of the impact of managerial and operational energy-influencing factors on energy consumption can be observed in practice due to irrational and inexplicable energy benchmarks. To this end, this paper presents a dual energy benchmarking methodology that specifically promotes energy awareness in terms of managerial and operational factors. The presented methodology uses data mining techniques to excavate energy data as well as managerial and operational data, consisting of three steps: (i) data preparation to provide reliable multi-source heterogeneous data; (ii) dynamic energy benchmarking that uses the decision tree algorithm to classify different energy consumption patterns; (iii) load shape benchmarking that interprets energy use behaviors based on load shape features. To demonstrate the effectiveness and practicality, the methodology was implemented in a die-casting workshop. The results showed that a total of nine dynamic energy benchmarks were created primality under the impact of capacity utilization, two of which generated five representative load curves to characterize the workshop's daily energy use behaviors. It was concluded that the methodology could provide practical recommendations on energy-efficient production planning and operation in practical applications.