Application of machine learning tools for energy efficiency in industry: A review

• Type: Another Publication in an International Scientific Journal
• Date: 2020

Authors

Narciso, DAC (Author) Other; Martins, FG (Author) FEUP

Journal

Title: Energy Reports

Vol. 6

Pages: 1181-1199

ISSN: 2352-4847

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-00S-3AB

DOI: 10.1016/j.egyr.2020.04.035

Abstract (EN): The current industrial context favors the generation of large amounts of data, most of which still seems to remain unexplored by the majority of enterprises. This paper presents a literature review on methodologies reported in the scientific literature exploring the potential value of industrial data via the utilization of Machine Learning tools for energy efficiency related goals. This work identifies and examines in detail the scientific contributions published up to date. A total of 42 published papers are found to present original contributions in this field, and addressing multiple energy efficiency challenges. A descriptive analysis is presented and demonstrates that the number of published works in this field is rapidly growing. The majority of contributions address challenges in petrochemical industries, and namely in ethylene production. There is still a very limited number of published papers addressing the application of Machine Learning tools on energy related objectives in other types of industries. The technical content of all identified papers is thoroughly reviewed and their key features and objectives are highlighted. A number of important themes across the final list of papers emerges, addressing challenges such as energy consumption forecast, energy analysis and energy optimization. A framework identifying the key goals reported on the set of 42 papers and the tools proposed to address them is suggested. This framework provides a summary on existing tools and facilitates the identification of research needs in this field. Additionally, the proposed framework serves as a reference guideline for the manufacturing and process industries on the selection of adequate Machine Learning tools for energy efficiency objectives via the utilization of industrial data. (C) 2020 The Authors. Published by Elsevier Ltd.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19