Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system

Musbah, Hmeda; Ali, Gama; Aly, Hamed H.; Little, Timothy

doi:10.1016/j.epsr.2021.107645

Cited by 35 publications

(18 citation statements)

References 51 publications

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“…Large industries are experimenting with a variety of schemes to stimulate and benefit from entrepreneurial activities outside their organizations. Information gathering, and analysis is still in progress, but it appears that while the general philosophy of open innovation is shared, there is considerable variation in how it is interpreted and applied, and a consensus on best practice has yet to emerge, as highlighted in similar approaches (e.g., in [55,56]).…”

Section: Discussionmentioning

confidence: 99%

An Equifinality Energy Management Framework in Terms of Benchmarking Practices and Expectations: The EnerMan Project Outlook

Katrakazas¹,

Costantino

Magnea

et al. 2021

Systems

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Background: The Industry 4.0 wave is leading the changes in existing manufacturing and industrial processes across the world. This is especially important in the formulation of the smart-factory concept with an outlook to energy sustainable processes. In viewing and identifying the foundational elements of such a transformation, the initial conditions and current practices in a cross-sectoral manner is considered a first, yet crucial step in the EU-funded project EnerMan. Methods: In this paper, we identify and analyse the key common features and characteristics of industrial practices set in a perspective of similar and identical functions with a focus to three key energy areas: sustainability, management, and footprint. The examination of different industrial sector cases is performed via distributed questionnaires and then viewed under the prism of the equifinality state via a text-mining analysis approach. Results: identification of common themes and benchmarking of current practices in a cross-industry manner led to the creation of a common systemic framework within energy management related aspects, which is hereby presented. Conclusions: use of an equifinality approach in energy management practices should be further pursued to open up new methods of ideation and innovation and communicate systems’ design in tandem with each industrial set goals.

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Section: Discussionmentioning

confidence: 99%

An Equifinality Energy Management Framework in Terms of Benchmarking Practices and Expectations: The EnerMan Project Outlook

Katrakazas¹,

Costantino

Magnea

et al. 2021

Systems

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show abstract

“…In the operational layer, the SEMS manages the load and sources in order to optimize the energy efficiency and improve the system as well, these operations reports to management layer where the energy manager can oversee all changes and can take real time decisions [22].…”

Section: Smart Energy Management System Architecturementioning

confidence: 99%

“…management layer where the energy manager can oversee all changes and can take real time decisions [22]. Figure 7 shows the general subsystems of a SEMS that can be implemented in industries, typically chemical processing and the mining industry.…”

Section: Smart Energy Management System Architecturementioning

confidence: 99%

“…The use of sophisticated machine learning, for an effective energy management strategy for microgrid, such as forecasting, and scheduling has shown very satisfactory results in power plants [21]. The same goes for hybrid energy systems, using the machine learning in order to choose the optimal combination from the available energy sources based on different criteria [22]. The goal is to apply the same approaches in the mining industry and combine solutions, by testing different algorithms then comparing the outcomes to select the algorithm with the best performance and to rank them in terms of both performance and accuracy [23].…”

Section: State Of Art Of the Smart Energy Management Systems 41 Introductionmentioning

confidence: 99%

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Smart Energy Management System: Design of a Monitoring and Peak Load Forecasting System for an Experimental Open-Pit Mine

Laayati

Bouzi

Chebak

2022

ASI

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Digitization in the mining industry and machine learning applications have improved the production by showing insights in different components. Energy consumption is one of the key components to improve the industry’s performance in a smart way that requires a very low investment. This study represents a new hardware, software, and data processing infrastructure for open-pit mines to overcome the energy 4.0 transition and digital transformation. The main goal of this infrastructure is adding an artificial intelligence layer to energy use in an experimental open-pit mine and giving insights on energy consumption and electrical grid quality. The achievement of these goals will ease the decision-making stage for maintenance and energy managers according to ISO 50001 standards. In order to minimize the energy consumption, which impact directly the profit and the efficiency of the industry, a design of a monitoring and peak load forecasting system was proposed and tested on the experimental open-pit mine of Benguerir. The main challenges of the application were the monitoring of typical loads machines per stage, feeding the supervisors by real time energy data on the same process SCADA view, parallel integrating hardware solutions to the same process control system, proposing a fast forest quantile regression algorithm to predict the energy demand response based on the data of different historical scenarios, finding correlations between the KPIs of energy consumption, mine production process and giving global insights on the electrical grid quality.

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“…speed optimization [12], alternative fuels [13], electrical power optimization [14][15][16] and other improvements have been recommended in the literature [7,17]. The use of methodologies like SEEMP and EEOI to measure energy efficiency in ships was developed by studies on the relationship between speed and main engine shaft power [18,19].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

et al. 2022

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In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship’s energy efficiency indexes appropriately. The algorithms’ prediction success was measured using the RMSE, MAE, and R2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R2, respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R2, respectively.

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Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system

Cited by 35 publications

References 51 publications

An Equifinality Energy Management Framework in Terms of Benchmarking Practices and Expectations: The EnerMan Project Outlook

An Equifinality Energy Management Framework in Terms of Benchmarking Practices and Expectations: The EnerMan Project Outlook

Smart Energy Management System: Design of a Monitoring and Peak Load Forecasting System for an Experimental Open-Pit Mine

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

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