A Review of Energy Management Assessment Models for Industrial Energy Efficiency

Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine

The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many of which have direct implications for humanity’s survival. The forecast of mine site energy use is one of the low-cost approaches for energy conservation. Accurate predictions do indeed assist us in better understanding the source of high energy consumption and aid in making early decisions by setting expectations. Machine Learning (ML) methods are known to be the best approach for achieving desired results in prediction tasks in this area. As a result, machine learning has been used in several research involving energy predictions in operational and residential buildings. Only few research, however, has investigated the feasibility of machine learning algorithms for predicting energy use in open-pit mines. To close this gap, this work provides an application of machine learning algorithms in the RapidMiner tool for predicting energy consumption time series using real-time data obtained from a smart grid placed in an experimental open-pit mine. This study compares the performance of four machine learning (ML) algorithms for predicting daily energy consumption: Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The models were trained, tested, and then evaluated. In order to assess the models’ performance four metrics were used in this study, namely correlation (R), mean absolute error (MAE), root mean squared error (RMSE), and root relative squared error (RRSE). The performance of the models reveals RF to be the most effective predictive model for energy forecasting in similar cases.

Economic and Production-Related Implications for Industrial Energy Efficiency: A Logistic Regression Analysis on Cross-Cutting Technologies

Increased industrial energy efficiency (EE) has become one of the main environmental actions to mitigate carbon dioxide (CO2) emissions, contributing also to industrial competitiveness, with several implications on the production system and cost management. Unfortunately, literature is currently lacking empirical evidence on the impact of energy efficiency solutions on production. Thus, this work primarily aims at investigating the economic and production-related influence on the reduction in industrial energy consumption, considering the cross-cutting technologies HVAC, motors, lighting systems and air compressor systems. The analysis is performed using data from previous studies that characterized the main EE measures for the cross-cutting technologies. Four logistic models were built to understand how costs and production influence energy efficiency across such cross-cutting technologies. In this way, motivating industries to implement measures to reduce electrical consumption, offering an economic cost–benefit analysis and optimizing industry processes so that the reduction in electricity consumption adds to industrial energy efficiency were the aims of this study. The results of this work show through the adjusted indicators that senior management is mainly responsible for energy savings. The operational measures of each piece of equipment can be oriented in the industry towards a specific maintenance process for each technology, becoming an active procedure in industrial productions to obtain EE. Additionally, maintenance planning and control is essential to the reliability of the reduced energy consumption of cross-cutting technologies. This article concludes with managerial implications and suggestions for future research in this field.

A Multifaceted Challenge to Enhance Multicriteria Decision Support for Energy Policy

The necessity to enhance multicriteria decision in the industry is challenging to support the current energy policy. European Union regulations and guidelines provide the guideline for minimalizing environmental harms but are not enough in their actions for providing effective sustainability assessment. None of the available standalone assessment methods do capture the comprehensibility of multicriteria decision-making. The aim of this paper is to demonstrate a challenge to incorporate the multicriteria sustainability decision-making method to mainstream energy policy, which is lacking in European Union policies. The novelty of the research lies in constructing a multicriteria sustainability approach for assessing energy technologies performance for embodying into a mainstream energy policy. In this study, the multicriteria decision-making—an approach combining life cycle-based methods, analytical hierarchy process, as well as macroeconomic analysis, was used to demonstrate the applicability of the method based on three photovoltaic technologies. The results showed that sustainability assessment supported with multicriteria decision allows to better understand analyzed factors influencing the energy technology, contributing to selection of the best sustainability technology according to the realization of an energy policy. It was proved based on a real example of photovoltaics, where string ribbon technology represents the most sustainable along its life cycle, with a 0.503 sustainability score. The study highlighted the challenge to embody the integrated method assessing sustainability-oriented technologies into an energy policy. This challenge regarding example evidence places emphasis on the decision-making process to realize an energy policy and in consequence, to improve enterprise sustainability performance.

Getting Municipal Energy Management Systems ISO 50001 Certified: A Study with 28 European Municipalities

Managing energy use by municipalities should be an important part of local energy and climate policy. The ISO 50001 standard constitutes an internationally recognized catalogue of requirements for systematic energy management. Currently, this standard is mostly implemented by companies. Our study presents an approach where consultants supported 28 European municipalities in establishing energy management systems. A majority (71%) of these municipalities had achieved ISO 50001 certification by the end of our study. We also conducted two surveys to learn more about motivations and challenges when it comes to establishing municipal energy management systems. We found that organizational challenges and resource constraints were the most important topics in this regard. Based on the experiences in our study we present lessons learned regarding supporting municipalities in establishing energy management systems.