Citation: Neves-Silva, R.;
Camarinha-Matos, L.M.
Context-Based Decision Support
System for Energy Efficiency in
Industrial Plants. Sustainability 2022,
14, 3885. https://doi.org/10.3390/
su14073885
Academic Editors: Adrián
Mota Babiloni, João Carlos de
Oliveira Matias and Paolo Renna
Received: 23 January 2022
Accepted: 22 March 2022
Published: 25 March 2022
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Article
Context-Based Decision Support System for Energy Efficiency
in Industrial Plants
Rui Neves-Silva * and Luis M. Camarinha-Matos
School of Science and Technology and Uninova CTS, NOVA University of Lisbon, Campus de Caparica,
2829-516 Caparica, Portugal; cam@uninova.pt
* Correspondence: rns@fct.unl.pt
Abstract:
Industrial companies must actively pursue more energy efficiency in their processes, with
impacts on both costs and the environment, and ultimately business performance. This article
explores the influence of context around the manufacturing process on energy consumption. By
creating awareness of this influence in a quantified way, it is possible, via a structured decision process,
to find opportunities and derive solutions to improve energy performance. This work introduces
a method developed in the scope of the LifeSaver project, which is based on the visualization of
energy consumption data against benchmark/average values. The overall approach is supported by
a software platform which offers a set of functionalities covering the complete approach, from the
detection of the consumption pattern to the implementation of improvement solutions. The approach
was tested in two industrial business cases. The first one illustrates the approach by showing the
influence of the human factor on the energy performance in cement production. The second case
deals with finding opportunities on the selection of the operation point, and its impact on peak load
management. The proposed approach and developed system demonstrate a positive direct impact on
reducing energy consumption and consequent carbon dioxide emissions. Furthermore, the operation
of the implemented case studies has an important indirect effect on bringing awareness to the impact
of small actions on general energy efficiency.
Keywords:
energy efficiency; decision support systems; context awareness; manufacturing industry
1. Introduction
Industrial companies need to be more energy efficient and reduce emissions; this has
a direct impact on costs, but also an indirect impact on the companies’ contribution to
sustainability. Such energy efficient objectives are in line with the goals of the UN 2030
Agenda for sustainability [
1
]. Although many organizations see the energy cost as a rigid
factor, it can be reduced by the acquisition of more efficient technologies and behavioral
changes [
2
,
3
]. While the need to reduce energy consumption and associated greenhouse gas
emissions is recognized by industry, there are still significant technical and non-technical
barriers to achieving this [
4
]. The main barriers are related to the lack of capital to invest
in new technologies and the existence of other priorities related to ensuring business
continuity [
5
]. Although there is a significant correlation between the use of technologies
for saving energy (and other resources) with environmental performance, this is just one
of many aspects affecting economic performance. Nonetheless, rising prices of energy
and growing environmental awareness of the markets is making energy efficiency a top
priority [
6
]. In fact, energy efficiency is the most cost-effective path to a cleaner future [
7
,
8
].
Furthermore, the efforts to restrain global carbon emissions and reverse the consequences
of climate change are equally important in these times of uncertainty. There is a strategic
opportunity to establish a sustainable path by taking advantage of the post-pandemic
recovery schemes to boost the climate agenda [9].
Currently, most industrial companies identify active participation in energy efficiency
programs as a recommended practice [
10
], particularly in what concerns monitoring energy
Sustainability 2022, 14, 3885. https://doi.org/10.3390/su14073885 https://www.mdpi.com/journal/sustainability
