Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing

Significance Statement

Eco-sustainability of industrial manufacturing has been considered as one of the building blocks of relations between the environment and people. The use of renewable energy has therefore become a fundamental element in view of this vision. A binding global accord was finally reached after several years of vain attempts to rule out an agreement to considerably curtail carbon dioxide emissions from burning fossil fuels.

A number of commonly used renewable energy sources, such as wind and solar, exhibit a problem concerning discontinuity in the production of energy owing to variability in weather as well as climatic conditions. Therefore, there has been increasing efforts by researchers to come up with a new methodology that can be capable of marrying industrial users’ instantaneous need for energy with the generation capability of renewable energy sources, and when necessary, supplemented by energy created via self-production and perhaps from third-party suppliers. This is in the view of minimizing carbon dioxide emissions as well as company energy costs.

In order to manage renewable energy sources effectively and efficiently, predictive models for industrial energy demands as well as production capacity of renewable energy sources is needed. University of Genoa researchers in Italy proposed to provide energy managers in the manufacturing environment with a support tool that can implement the potentialities of Discrete Event Simulation as well as Monte Carlo simulation and incorporated with a unique predictive algorithm to allow optimizing energy supplying mix. Their research work is published in Applied Energy.

Tackling the issue of the supplemented as well as optimal use of energy produced by renewable energy sources in the field of manufacturing, the authors proposed a management method referenced on two steps, fortified by two respective models; the Energy Resources Intelligent Management-Predictor (ERIM-P) and Energy Resources Intelligent Management-Real Time (ERIM-RT). The purpose of the former model was to come up with, 24h in advance, the hourly electrical energy requirement of the manufacturing plant referenced on the production plan made for the next day. The model was also to quantify the possible self-production of renewable energy sources energy based on weather forecast for the next day.

Upon completion of the first model, the latter ERIM-RT will act on the current day taking into consideration through the implementation of an on-line real-time Discrete Event Simulation simulator of what would be happening in real time with the manufacturing plant and the actual instantaneous generation of renewable energy sources. The use of a predictive algorithm would offer a 30min update of the available renewable energy generation prediction for subsequent times of the day.

Counting on the test cases done on the tannery, the outcomes observed showed that the ERIM-RT model allowed for obtaining considerable improvements in real time estimates, both real photovoltaic generation and daily energy demand schedule. In the combined high-variability sub-scenarios, the authors found a clear enhancement in energy performance for the tannery in view of reduction of error, carbon-dioxide emissions, and energy costs. The model was found to be more effective when the larger the deviations were between the prediction made on the day before and the real profiles for the current day.

Their study also highlighted that the more the attributes of the tannery were affected by randomness, the more the need for the ERIM-RT model became essential. With the two sub-scenarios, Demand Lower Production Higher and Demand Higher Production Lower, the ERIM-RT model led to enhancement in predictive performance by, respectively, 7.3 and 7 times greater than with the ERIM-P model alone.


Lucia Cassettari, Ilaria Bendato, Marco Mosca, and Roberto Mosca. Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing. Applied Energy, volume 190 (2017), pages 841–851.

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