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“the Human Factor In Energy Efficiency: Behavioral Insights”

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Manage Your Energy, Not Your Time

By Rui Neves-Silva Rui Neves-Silva Scilit Preprints.org Google Scholar * and Luis M. Camarinha-Matos Luis M. Camarinha-Matos Scilit Preprints.org Google Scholar

School of Science and Technology and Uninova CTS, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal

Received: 23 January 2022 / Revised: 16 March 2022 / Accepted: 22 March 2022 / Published: 25 March 2022

Industrial companies must actively pursue more energy efficiency in their processes, with impacts on both costs and the environment, and ultimately on business performance. This article explores the influence of the context surrounding the manufacturing process on energy consumption. By creating an awareness of this influence in a quantified way, it is possible, through a structured decision process, to find opportunities and derive solutions to improve energy performance. This work introduces a method developed within the scope of the LifeSaver project, which is based on the visualization of energy consumption data against reference/average values. The overall approach is supported by a software platform that offers a set of functionalities covering the entire approach, from the discovery 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 the finding of opportunities on the choice of the point of operation, and its impact on the management of the maximum load. The proposed approach and the developed system show a direct positive impact on the reduction of energy consumption and the consequent carbon dioxide emissions. Furthermore, the running of the case studies implemented has an important indirect effect in bringing awareness to the impact of small actions on overall energy efficiency.

Eden About Phase Iii

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 objectives of the UN 2030 Agenda for sustainability [1]. Although many organizations see the cost of energy as a rigid factor, it can be reduced by the acquisition of more efficient technologies and changes in behavior [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 obstacles 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 only one of the many aspects that affect economic performance. However, the increase in energy prices and the increasing environmental awareness of the markets are making energy efficiency a top priority [6]. In fact, energy efficiency is the most cost-effective path to a cleaner future [7, 8]. In addition, efforts to curb global carbon emissions and reverse the consequences of climate change are equally important in these uncertain times. There is a strategic opportunity to set a sustainable path by taking advantage of 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 terms of monitoring energy consumption, verifying -use of raw materials and carbon dioxide emissions, and the integration of these activities in a decision support system. to mitigate risks. In fact, the development of decision support systems for energy saving has been attracting attention.

In addition, the European energy policy has contributed with its clear orientation towards the preservation of energy and the improvement of the environmental quality inside the building through the adoption of the Directive of the European Commission on the Energy Performance of Buildings [11]. As a result, there have been significant efforts towards the design, operation, and maintenance of energy efficient and environmentally conscious buildings. In addition, some research has been done in the development of decision support systems for environmental management [12, 13].

Some of the approaches developed for building can be adopted for industry, but the specifics of the problems are obviously different and require tailor-made solutions. Consequently, there are not many systems developed specifically for the industrial domain, and the existing ones are based on the modeling and simulation of industrial facilities, without taking into account important aspects such as keeping the decision-making criteria aligned with business goals [14]. On the other hand, in recent decades, industrial companies have been investing in improving the energy efficiency of their production units guided by existing “recipes” based on the rules to reduce the consumption of -energy for almost all the different industrial sectors. Therefore, the plant managers feel that somehow the work is done and there is little room for improvement. However, experience tells us that, here and there, new opportunities for optimization can be found outside and beyond the usual prescribed solutions. Because the new opportunities and the corresponding measures are specific to a particular plant, they do not appear in the general manuals of energy efficiency. For example, the way plants are operated by human actors [15], or the influence of raw material characteristics, are specific to the plant, but can have relevant impacts on energy consumption. A well-structured, multi-criteria decision-making approach has also been used in the development of energy management frameworks with the aim of creating decision support mechanisms for complex situations [16].

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Therefore, this article is focused on these indirect and plant-specific factors that can contribute to reducing energy consumption in manufacturing processes. All these indirect factors together represent the context in which a plant is operating. Becoming aware of how context variables and hidden relationships affect energy consumption and corresponding emissions is the first step to finding new opportunities for improvement.

This research work started within the scope of the LifeSaver project [17]. Its overall objective was to develop a method and support platform to help companies optimize their operations and allow them to increase energy savings (and thus reduce carbon dioxide emissions) to beyond the usual measures available. The basic assumption and innovation aspect of the LifeSaver Project was precisely the use of “contextual information”, in addition to the usual process data, to support decision making with positive impact on energy efficiency and emissions. This approach, based on finding additional explanations about the context of the operations, allows to address the highest level of energy efficiency beyond the classical prescriptions prescribed. In LifeSaver, support is provided in the form of a set of software building blocks that combine context awareness, environmental intelligence monitoring, and standard data measurement of the energy consumption. It provides (i) comprehensive information on energy consumption to be processed in enterprise management systems, with the aim of achieving energy savings; (ii) a knowledge-based decision support system for optimizing the energy performance of operations; and (iii) appropriate and forecasted near-online cumulative data on carbon emissions, as input to decision support services to enable the exchange of emission allowances within industries and among companies. Of course, these three project results are interconnected, and any improvement in each of them has a positive impact on the other two. The work presented here refers to the second listed result of the project, on how to support the decision process towards the energy performance of the operations.

To effectively use context awareness to make informed decisions, predictability is a key factor. Having knowledge of additional factors that contribute to energy consumption allows plant managers to plan production more efficiently and effectively. An example of this ability to improve planning by involving additional factors from our daily lives are modern car navigation systems. The estimated time of

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