State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Mosavi, Amir; Salimi, Mohsen; Ardabili, Sina Faizollahzadeh; Rabczuk, Timon; Shamshirband, Shahaboddin; Várkonyi-Kóczy, Annamária R.

doi:10.3390/en12071301

Cited by 349 publications

(133 citation statements)

References 105 publications

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“…Digital technologies such as blockchain, artificial intelligence or cloud computing and their applications in the energy sector are, for example, discussed in [21][22][23][24][25][26][27][28][29]. The collected edition [21] gives a broad overview of the digitalization and cloud applications across different economic sectors based on articles by researchers, journalists, and business representatives.…”

Section: The Current State Of Knowledgementioning

confidence: 99%

“…The use of artificial intelligence in the energy sector is the focus of the publications [28,29]. Both articles give an overview of practical-use cases based on a literature review and conclude that artificial intelligence and machine learning can greatly increase the accuracy of demand, generation, and price forecasting and thereby support the implementation of "smart grids" and the integration of more renewable energy.…”

Section: The Current State Of Knowledgementioning

confidence: 99%

“…Both articles give an overview of practical-use cases based on a literature review and conclude that artificial intelligence and machine learning can greatly increase the accuracy of demand, generation, and price forecasting and thereby support the implementation of "smart grids" and the integration of more renewable energy. An assessment and a structure of different machine learning algorithms is presented in [28].…”

Section: The Current State Of Knowledgementioning

confidence: 99%

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Review and Categorization of Digital Applications in the Energy Sector

Weigel

Fischedick

2019

Applied Sciences

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Digitalization is a transformation process which has already affected many parts of industry and society and is expected to yet increase its transformative speed and impact. In the energy sector, many digital applications have already been implemented. However, a more drastic change is expected during the next decades. Good understanding of which digital applications are possible and what are the associated benefits as well as risks from the different perspectives of the impacted stakeholders is of high importance. On the one hand, it is the basis for a broad societal and political discussion about general targets and guidelines of digitalization. On the other hand, it is an important piece of information for companies in order to develop and sustainably implement digital applications. This article provides a structured overview of potential digital applications in the German energy (electricity) sector, including the associated benefits and the impacted stakeholders on the basis of a literature review. Furthermore, as an outlook, a methodology to holistically analyze digital applications is suggested. The intended purpose of the suggested methodology is to provide a complexity-reduced fact base as input for societal and political discussions and for the development of new digital products, services, or business models. While the methodology is outlined in this article, in a follow-up article the application of the methodology will be presented and the use of the approach reflected.Good knowledge of the expected digital applications and how benefits and potential downsides affect different stakeholders is an essential basis for a broad societal and political discussion to set targets and guidance for digital transformation. Furthermore, this knowledge is relevant for the development of new business models. Therefore, the benefits, as well as potential risks and bottlenecks from the perspective of different stakeholders, need to be analyzed early on to develop solution options for pitfalls and ensure that the full benefits can be utilized.As a first step, transparency on which digital applications can be expected in the electricity sector and why they will/might be implemented needs to be created. Therefore, this article presents the results of a literature review of ten publications with the following three objectives:

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Section: The Current State Of Knowledgementioning

confidence: 99%

Section: The Current State Of Knowledgementioning

confidence: 99%

Section: The Current State Of Knowledgementioning

confidence: 99%

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Review and Categorization of Digital Applications in the Energy Sector

Weigel

Fischedick

2019

Applied Sciences

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“…The rapidly evolving industries have suggested that we will be witnessing further increase this rate [7][8][9][10][11][12]. Furthermore, the increasing demand for the hybrid and electric vehicles, the rapid transition toward automated systems and micro and nano mechatronics devises, increasing interests for more efficient energy conversion systems, and emerging new robotics machines have been motivating further advancement in the rotating electrical machines [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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“…Wang et al, 2003), bubble characteristics (Essadki, Nikov, & Delmas, 1997;Lapin, Paaschen, Junghans, & Lübbert, 2002;H Li & Prakash, 1999Prakash et al, 2001;Schäfer, Merten, & Eigenberger, 2002), flow regimes and computational fluid dynamics (Buwa & Ranade, 2002;Degaleesan et al, 2001;M. Dhotre, Ekambara, & Joshi, 2004;Michele & Hempel, 2002;Ruzicka, Zahradnık, Drahoš, & Thomas, 2001; Mosavi et al 2019), local and mean heat transfer determinations (Chen, Hasegawa, Tsutsumi, Otawara, & Shigaki, 2003;Cho, Woo, Kang, & Kim, 2002;Hanning Li & Prakash, 2001;H Li & Prakash, 2002;Lin & Wang, 2001), and mass transfer (Behkish, Men, Inga, & Morsi, 2002;Maalej, Benadda, & Otterbein, 2003;Vandu & Krishna, 2004;Verma & Rai, 2003).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

Mosavi¹,

Chau²

2019

Preprint

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Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to 2 > 0.99 almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm.This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Cited by 349 publications

References 105 publications

Review and Categorization of Digital Applications in the Energy Sector

Review and Categorization of Digital Applications in the Energy Sector

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

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