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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

3.6K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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Related Experiment Videos

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction

Hao Quan, Dipti Srinivasan, Abbas Khosravi

    IEEE Transactions on Neural Networks and Learning Systems
    |December 23, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Integrating renewable energy into smart grids introduces uncertainties. This study uses neural network prediction intervals and Monte Carlo simulations for robust wind power forecasting, enhancing system reliability and reducing operational risks.

    Related Experiment Videos

    Area of Science:

    • Electrical Engineering
    • Power Systems
    • Artificial Intelligence

    Background:

    • Renewable energy integration increases operational uncertainties in smart grids.
    • Accurate forecasting and uncertainty quantification are crucial for grid stability and reliability.

    Purpose of the Study:

    • To develop a robust method for quantifying wind power forecast uncertainties.
    • To improve the security-constrained unit commitment (SCUC) model for smart grids with high renewable energy penetration.

    Main Methods:

    • Nonparametric neural network-based prediction intervals (PIs) for uncertainty quantification.
    • Scenario generation using empirical cumulative distribution functions (ECDF) and Monte Carlo simulation.
    • Stochastic security-constrained unit commitment (SCUC) model solved with a genetic algorithm.

    Main Results:

    • The proposed stochastic SCUC model demonstrates greater robustness compared to deterministic approaches.
    • The method effectively incorporates wind power forecast uncertainties into unit commitment decisions.
    • Reduced operational risks and improved system reliability were observed in case studies.

    Conclusions:

    • The integration of advanced forecasting techniques enhances the management of renewable energy uncertainties in smart grids.
    • Stochastic optimization provides a more reliable framework for unit commitment than deterministic methods.
    • The study offers a practical approach to mitigate risks associated with variable renewable energy sources.