A Novel Methodology for Developing an Advanced Energy-Management System

Cristian Gheorghiu (), Mircea Scripcariu, Gabriela Nicoleta Tanasiev, Stefan Gheorghe and Minh Quan Duong
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Cristian Gheorghiu: Power Generation and Use Department, Power Engineering Faculty, University “POLITEHNICA” of Bucharest, Splaiul Independenței No. 313, District 6, RO-060042 Bucharest, Romania
Mircea Scripcariu: Power Generation and Use Department, Power Engineering Faculty, University “POLITEHNICA” of Bucharest, Splaiul Independenței No. 313, District 6, RO-060042 Bucharest, Romania
Gabriela Nicoleta Tanasiev: Power Generation and Use Department, Power Engineering Faculty, University “POLITEHNICA” of Bucharest, Splaiul Independenței No. 313, District 6, RO-060042 Bucharest, Romania
Stefan Gheorghe: Power Generation and Use Department, Power Engineering Faculty, University “POLITEHNICA” of Bucharest, Splaiul Independenței No. 313, District 6, RO-060042 Bucharest, Romania
Minh Quan Duong: Electrical Engineering Faculty, The University of Da Nang—University of Science and Technology, 54 Nguyen Luong Bang Street, Danang City 550000, Vietnam

Energies, 2024, vol. 17, issue 7, 1-34

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.

Keywords: energy efficiency; power quality; renewable energy sources; energy management systems; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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