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関連する概念動画

Prediction Intervals01:03

Prediction Intervals

2.3K
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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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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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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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Uncertainty: Overview00:59

Uncertainty: Overview

976
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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Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

Design Example: Calculating Safe Diameter for Wind-Exposed Disc

177
Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
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Updated: Sep 9, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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風速の不確実性の確率的機械学習ベースの予測は,アダプティブカーネル密度推定を使用しています.

Rami Al-Hajj1

  • 1College of Engineering and Technology, American University of the Middle East, Kuwait.

Mathematical biosciences and engineering : MBE
|September 3, 2025
PubMed
まとめ
この要約は機械生成です。

再生可能エネルギーには 短期的な風速予測が不可欠です この研究では,精密な予測間隔のためのSVR-AKDEモデルによるハイブリッドサポートベクトル回帰が導入され,風力エネルギーの信頼性が向上します.

キーワード:
AKDE についてSVR についてアダプティブ・カーネル・デンシティー・エスティメーター予測区間確率的なエネルギー予測再生可能エネルギーサポートベクトルリグレッサー風速の予測

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科学分野:

  • 再生可能エネルギーシステム
  • 機械学習アプリケーション
  • 統計予測

背景:

  • 効率的な風力エネルギー統合には 短期的な風速予測が不可欠です
  • 風速の不確実性を捉えるのに 標準的な点予測は不正確です
  • 予測の不確実性を定量化することは,信頼性の高い風力発電事業に不可欠です.

研究 の 目的:

  • 短期的な風速予測間隔のためのハイブリッド予測方法論を開発する.
  • サポートベクトル回帰 (SVR) とアダプティブカーネル密度推定 (AKDE) を使用して予測の不確実性を定量化します.
  • 提案されたSVR-AKDEモデルを,より優れた不確実性推定のための従来の方法と比較して評価する.

主な方法:

  • サポートベクトル回帰 (SVR) とアダプティブカーネル密度推定 (AKDE) を組み合わせたハイブリッドモデルが開発されました.
  • 正確な不確実性の定量化のために,ローカル予測エラー分布に基づいて帯域幅を調整するために,アダプティブKDEが使用されました.
  • SVR-AKDEモデルは短期間 (10,30,60,120分) で評価された.

主要な成果:

  • SVR-AKDEモデルは,風速予測区間を推定する上で優れたパフォーマンスを示しました.
  • 提案された方法は一貫して,予測区間のカバー確率 (PICP) を向上させ,予測区間の正規化された平均幅 (PINAW) を狭めました.
  • シミュレーション結果は,従来のKDEベースの間隔推定よりもSVR-AKDEの有効性を確認しました.

結論:

  • SVR-AKDEハイブリッドモデルは,定量的不確実性を持つ短期的な風速予測のための堅固な解決策を提供します.
  • このアプローチは,風力発電施設の信頼性と運用管理を強化します.
  • 風力発電の潜在力を最大限に生かすには 精密な不確実性の量化が重要です