A Novel Methodology for Developing an Advanced Energy-Management System

Highlights What are the main findings? The use of machine learning algorithms in energy management services can lead to a significant increase in the implementation rate of energy performance, power quality and renewable energy sources projects; Integrating machine learning algorithms in the process of assessing the energy saving potential can accelerate the deployment of energy performance contracting; What is the implication of the main finding? Digitization of the energy services sector could support end-users in achieving their targets regarding the transition towards environmental sustainability Policy makers could also use the proposed methodology to evaluate the global energy performance of the relevant energy sectors, thus increasing the performance of the available financing mechanisms.Highlights What are the main findings? The use of machine learning algorithms in energy management services can lead to a significant increase in the implementation rate of energy performance, power quality and renewable energy sources projects; Integrating machine learning algorithms in the process of assessing the energy saving potential can accelerate the deployment of energy performance contracting; What is the implication of the main finding? Digitization of the energy services sector could support end-users in achieving their targets regarding the transition towards environmental sustainability Policy makers could also use the proposed methodology to evaluate the global energy performance of the relevant energy sectors, thus increasing the performance of the available financing mechanisms.Abstract Current targets, which have been set at both the European and the international level, for reducing environmental impacts and moving towards a sustainable circular economy make energy efficiency and digitization key elements of all sectors of human activity. The authors proposed, developed, and tested a complex methodology for real-time statistical analysis and forecasting of the following main elements contributing to the energy and economic performance of an end user: energy performance indicators, power quality indices, and the potential to implement actions to improve these indicators, in an economically sustainable manner, for the end user. The proposed methodology is based on machine learning algorithms, and it has been tested on six different energy boundaries. It was thus proven that, by implementing an advanced energy management system (AEMS), end users can achieve significant energy savings and thus contribute to the transition towards environmental sustainability.