Bayesian networks in renewable energy systems: A bibliographical survey

Borunda, Mónica; Jaramillo, O.A.; Reyes, Alberto; Ibargüengoytia, Pablo H.

doi:10.1016/j.rser.2016.04.030

Cited by 70 publications

(27 citation statements)

References 63 publications

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“…To solve such problems, there can be several alternatives, each of which (according to the BN model) is assigned a probability (which can be obtained via statistical analysis of the available data); they are treated according to probability theory (Bayes' rules), which is why this AI model is also known as a probabilistic network, as presented by [173,174]. The BN structure follows the Markov chain properties [175].…”

Section: Bayesian Networkmentioning

confidence: 99%

“…To predict the failures of the wind turbine components (blades, gearbox, generator, main bearing, pitch and yaw), in [179], the BN model is used, trained with the available data on meteorological variables, failure records and the technology used in WTs. In [175], the authors perform a specific study regarding BNs and include a section in which a considerable amount of applications to different areas of the wind industry can be found.…”

Section: Bayesian Networkmentioning

confidence: 99%

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Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

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Section: Bayesian Networkmentioning

confidence: 99%

Section: Bayesian Networkmentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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“…In addition to these efforts, Table 2 presents a summary of nine [1,2,[50][51][52][53][54][55] of most recent attempts to overview latest EPDM solutions. The tabular overview covers similar aspects as those discussed in previous reviews, along with the total number of included articles, the covered period(s) of the publications, the main characteristics of the review, the authors' summary of results, and most importantly the authors' final conclusions.…”

Section: An Overview Of Reviews On Epdmmentioning

confidence: 99%

“…while targeting only a particular EPDM solution (e.g., bayesian networks [50], MOO [2,5,6], MCA [6,10,57], fuzzy and FMCDM approaches [60], etc.). In other words, none of these works cover all EPDM challenges and their related solutions.…”

Section: An Overview Of Reviews On Epdmmentioning

confidence: 99%

“…Additionally, this paper integrates results and outcomes from some recent [1,2,6,[50][51][52][53][54][55][56] and most cited literature reviews [5, 9-11, 39, 57-60], to: (i) formulate essential evidence as well as practical and conclusive literature's support-alongside with the formulated representative sample -to this paper's subsequent insights and statements; and (ii) guarantee that no relevant articles have been excluded. Moreover, differences between previous literature reviews in this area of research and our proposed work are explained.…”

Section: Introductionmentioning

confidence: 99%

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Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Sellak¹,

Ouhbi²,

Frikh

et al. 2017

Renewable and Sustainable Energy Reviews

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Achieving sustainable energy planning and development involves complex decision-making processes. The energy planning decision-making (EPDM) field relies on a plethora of decision analysis methods that offered many solutions to process a variety of energy management and strategic decision-making problems. However, current EPDM solutions are unable to overcome the increasing complexity of strategic energy planning situations involving a large number of stakeholders in uncertain, dynamic, and distributed environments. This raises significant new challenges for researchers in both decision sciences and renewable and sustainable energy planning. On the basis of a representative assortment of peer-reviewed related literature selected by querying multiple electronic databases and indexed in Scopus and Web of Science databases domain journals over the last 12 years, this paper exhaustively highlights and discusses limitations of existing strategic EPDM solutions. The analysis is based on a classification specially developed by holistically harmonizing important domain concepts to categorize the considered representative sample of the field of interest. Additionally, this paper integrates results and conclusions from some recent and most cited literature reviews to (i) formulate essential evidence as well as practical and conclusive literature's supportalongside with the formulated representative sample -to this paper's subsequent insights and statements, and (ii) guarantee that no relevant articles have been excluded. A total of 78 related works is gathered and analyzed to provide a general view and discussion on major complexities found in classical/traditional strategic EPDM solutions and challenges for next-generation EPDM solutions. Moreover, a comparative analysis of the two solutions and a set of "quality indexes" of a next-generation EPDM solution were identified and some proposals were made to improve future applicative research. As an original result coming from the "quality indexes" identified through the review process, an intelligent expert-based framework for next-generation EPDM solutions is developed for enhanced renewable and sustainable energy planning.

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Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

Afridi

Ahmad

Hassan

2021

Intl J of Energy Research

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Summary Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures.

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Bayesian networks in renewable energy systems: A bibliographical survey

Cited by 70 publications

References 63 publications

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

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