A Review of Energy Management Assessment Models for Industrial Energy Efficiency

Hasan, Alaa; Trianni, Andrea

doi:10.3390/en13215713

Cited by 34 publications

(13 citation statements)

References 78 publications

(129 reference statements)

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“…Indeed, the identified facts are very critical to drive and implement not only energy management but also many relevant issues in the broader umbrella of energy. Moreover, the findings also ascertain and emphasize multi-dimensional approaches to industrial energy management [89]. In many cases, energy management are not considered comprehensively in the industries.…”

Section: Drivers For Energy Efficiency 61 Analysis Of Drivers By Average Score and Frequency Of Responsesmentioning

confidence: 90%

A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh

Hasan

Tuhin

Ullah³

et al. 2021

Energy

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Industrial energy efficiency is acknowledged as a cost-effective mean contributing to sustainable development and industrial competitiveness. Implementing energy management practices becomes even more imperative for developing countries, considering their energy usage trends and economic development forecasts. Based on the circumstances, an empirical investigation is conducted on energy efficiency and management practices, as well as barriers and drivers to energy efficiency in the energyintensive industries of Bangladesh. The study finds that majority of the companies barely implement the energy management practices. Energy audits represent the mostly implemented energy management practice at the industries, though a comprehensive approach on a detailed level is still lacking. In addition, this study finds that the number of dedicated and specialised energy professionals employed in the industries is yet negligible. The cumulated results show that energy efficiency is mostly disrupted due to inadequate support from preeminent administration and bureaucratic intricacy. Energy blueprint cost-saving due to less use of energy and rules and regulations were distinctively signified as most imperative drivers for energy efficiency. On the other hand, lack of information is found to be the most significant barrier to consult energy service companies. Analysis of the country's energy usage and supply-demand relationship points towards insufficient energy efficiency measures and energy management practices in the country. The study also finds that energy efficiency could be improved by 8% to 10% through the practice of energy management. Our findings, besides pointing out specific issues to be tackled in the specific context of investigation, pave the way for further research over industrial energy efficiency in developing countries.

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Section: Drivers For Energy Efficiency 61 Analysis Of Drivers By Average Score and Frequency Of Responsesmentioning

confidence: 90%

A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh

Hasan

Tuhin

Ullah³

et al. 2021

Energy

Self Cite

View full text Add to dashboard Cite

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“…A consultant keeps supporting the municipalities during this phase when needed. The importance of continuous improvement of energy management practices was stipulated in the ISO 50001 standard [3], but was also widely discussed in earlier scientific studies on energy management [4,8].…”

Section: Methodsmentioning

confidence: 99%

“…Most papers on ISO 50001 EnMS deal with energy management systems in companies, for which even a standardized protocol for energy assessment has been developed [7]. Regarding energy management in industrial companies, several assessment models have been proposed and put to test [8]. In general, high hopes have been placed on the possible impacts of successful energy management.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…However, the integration of energy management into the industrial decision-making process has received little attention. As a result, it is critical to investigate the domain of energy management in order to assist industrial decision-makers in identifying the specific actions required to reduce energy management lags [9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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A Review of Energy Management Assessment Models for Industrial Energy Efficiency

Cited by 34 publications

References 78 publications

A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh

A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh

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

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

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