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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Updated: Jun 30, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Investigation of Data Size Variability in Wind Speed Prediction Using AI Algorithms.

M A Ehsan1, Amir Shahirinia1, Nian Zhang1

  • 1Department of Electrical and Computer Engineering, University of the District of Columbia, Washington, DC, USA.

Cybernetics and Systems
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Summary
This summary is machine-generated.

Predicting wind speed using artificial intelligence algorithms is crucial for renewable energy planning. The best AI model for wind speed forecasting depends on the size of the available dataset.

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Convolutional neural networksdeep learninglong short-term memory (LSTM)wind speed prediction

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence Applications
  • Environmental Science

Background:

  • Fossil fuel energy generation significantly contributes to global warming.
  • Renewable energy offers a sustainable alternative, reducing industrial emissions.
  • Decentralized energy production enhances energy independence.

Purpose of the Study:

  • To predict wind speed for improved wind farm planning and feasibility.
  • To evaluate the accuracy of various artificial intelligence algorithms for wind speed prediction.

Main Methods:

  • Collected meteorological data was utilized.
  • Twelve distinct artificial intelligence algorithms were employed for wind speed prediction.
  • Model performance was assessed based on prediction accuracy across different dataset sizes.

Main Results:

  • The study compared the performance of twelve artificial intelligence algorithms.
  • Wind speed prediction accuracy varied among the algorithms.
  • The optimal algorithm for wind speed prediction was found to be data-size dependent.

Conclusions:

  • Accurate wind speed prediction is essential for effective wind energy integration.
  • Artificial intelligence offers powerful tools for forecasting intermittent renewable resources like wind.
  • Selecting the appropriate AI model based on data availability is key to successful wind farm development.