Review of forecasting methods to support photovoltaic predictive maintenance

Ramirez-Vergara, Jose; Bosman, Lisa; Wollega, Ebisa; Leon-Salas, Walter D.

doi:10.1016/j.clet.2022.100460

Cited by 19 publications

(4 citation statements)

References 93 publications

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“…A study by (Ghimire et al, 2019) also indicated the non-stationarity of the Solar flux variable and the need for data transformation before its inclusion in the ARIMA model. To strengthen these findings, researchers can cite studies such as those conducted by (Ramirez-Vergara et al, 2022) and (Ghimire et al, 2019), which confirm the nonstationarity of Solar flux and the requirement for data transformation before inclusion in the ARIMA model. Such citations would enhance confidence in the research findings.…”

Section: Discussionmentioning

confidence: 97%

Relationship between Solar Flux and Sunspot Activity Using Several Regression Models

Siagian

Alfaris²,

Ahmad

et al. 2023

J. Ilmu Fis.

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This study examines the correlation and prediction between sunspots and solar flux, two closely related factors associated with solar activity, covering the period from 2005 to 2022. The study utilizes a combination of linear regression analysis and the ARIMA prediction method to analyze the relationship between these factors and forecast their values. The analysis results reveal a significant positive correlation between sunspots and solar flux. Additionally, the ARIMA prediction method suggests that the SARIMA model can effectively forecast the values of both sunspots and solar flux for a 12-period timeframe. However, it is essential to note that this study solely focuses on correlation analysis and does not establish a causal relationship. Nonetheless, the findings contribute valuable insights into future variations in solar flux and sunspot numbers, thereby aiding scientists in comprehending and predicting solar activity's potential impact on Earth. The study recommends further research to explore additional factors that may influence the relationship between sunspots and solar flux, extend the research period to enhance the accuracy of solar activity predictions and investigate alternative prediction methods to improve the precision of forecasts.

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Section: Discussionmentioning

confidence: 97%

Relationship between Solar Flux and Sunspot Activity Using Several Regression Models

Siagian

Alfaris²,

Ahmad

et al. 2023

J. Ilmu Fis.

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“…PdM can also help to identify potential problems with energy infrastructure, such as leaks or corrosion. This information can be used to take corrective action before a problem causes a major outage [24], [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Mechanisms for PdM in Automotive Machinery: A Comprehensive Analysis using ML/DL

Snehal A. Patil

2024

jes

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Predictive maintenance (PdM) technique involves analyzing and utilizing data to identify problems before they happen. It can help prevent costly repairs and downtime. In the past few years, the use of intelligent tools for PdM in automotive machinery has been increasing. These tools can be used to analyze and collect data from various sources, such as cloud computing and sensors. In the prediction of failures, this data can be used in combination with machine learning algorithms. With the help of advanced technologies, such as machine learning and sensors, PdM has become a viable option to maintain machinery while minimizing costs and downtime. The paper presents a comprehensive analysis of the various components of the intelligent tools that are used for PdM. It starts by exploring the different kinds of sensors and their functions in monitoring the condition of the equipment. The paper then explores the synergistic relationship between machine learning and data analytics, demonstrating how these technologies can help identify potential issues, predict the remaining useful life of the equipment, and detect early anomalies. The paper reviews the literature on the use of intelligent tools and sensors for PdM in automotive machinery. It delves into the diverse kinds of mechanisms that have been employed for this type of PdM, the pros and cons of using such tools, as well as the possible directions in this domain. Despite the various challenges that have been presented, the potential of implementing intelligent tools in automotive machinery is still immense. They can help prevent equipment downtime and improve the safety and efficiency of the operations of the machinery. As the technology matures, we can expect the adoption of such mechanisms to increase. The report emphasizes the significant contribution of intelligent tools and sensors to the optimization of the maintenance schedules and the reduction of unplanned downtime in automotive machinery. The findings of this study provide a roadmap for practitioners, researchers, and industrial organizations looking to harness the potential of such mechanisms to guarantee the longevity of their assets.

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“…There are many traditional techniques used for statistical forecasting, the most famous are linear regression and Autoregressive Integrated Moving Average (ARIMA). The main problem with this methodology is the data standards and volume used to perform and obtaining a specific level of quality (RAMIREZ-VERGARA et al, 2022).…”

Section: Forecast Modelsmentioning

confidence: 99%

Detecção De Avarias Em Isoladores De Potência: Uma Pesquisa Da Literatura Correlata À Utilização De Sistemas Inteligentes

Silva¹,

Tavares²,

Lopes³

et al. 2022

Engenharia Elétrica: Sistemas De Energia Elétrica E Telecomunicações

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Direitos para esta edição cedidos à Atena Editora pelos autores. Open access publication by Atena Editora Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0). O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

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Review of forecasting methods to support photovoltaic predictive maintenance

Cited by 19 publications

References 93 publications

Relationship between Solar Flux and Sunspot Activity Using Several Regression Models

Relationship between Solar Flux and Sunspot Activity Using Several Regression Models

Intelligent Mechanisms for PdM in Automotive Machinery: A Comprehensive Analysis using ML/DL

Detecção De Avarias Em Isoladores De Potência: Uma Pesquisa Da Literatura Correlata À Utilização De Sistemas Inteligentes

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