Estimation of wind speed with artificial neural networks method for Isparta using meteorological measurement data

Gemici, Fatih; Şahin, Arzu Şencan

doi:10.31593/ijeat.895362

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…During cable production, all processes of a cable were examined. Errors encountered after the examination were determined [5][6][7][8]. Probability, severity, and discoverability values were given to these errors and RPN values were calculated.…”

Section: Introductionmentioning

confidence: 99%

Integrated Risk Assessment Analysis with Fuzzy Logic

2022

msom

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In a developing business, it has a very important to share in terms of competitive advantage by detecting and directing the errors before occur. There are many methods in the literature for the early detection and prioritization of these failures. Failure modes and effects analysis (FMEA) is also a common method of choice. The uncertainty and flexibility problem arising from error types analysis has been eliminated by integrating fuzzy FMEA. The probability, severity, and discoverability values determined for each error were examined with error types, effects and fuzzy logic methods. Probability, severity, and discoverability values are considered and analyzed. Each method was listed according to the determined risk process network values and expert opinion, and comparisons were made between the methods.

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Section: Introductionmentioning

confidence: 99%

Integrated Risk Assessment Analysis with Fuzzy Logic

2022

msom

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Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye

Sen,

Andic,

Aydin

et al. 2023

2023 14th International Conference on Electrical and Electronics Engineering (ELECO)

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The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

ŞENKAL,

EMEKSİZ

2023

International Scientific and Vocational Studies Journal

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This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities.

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Estimation of wind speed with artificial neural networks method for Isparta using meteorological measurement data

Cited by 3 publications

References 15 publications

Integrated Risk Assessment Analysis with Fuzzy Logic

Integrated Risk Assessment Analysis with Fuzzy Logic

Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye

The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

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