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風速予測のための遺伝的アルゴリズムベースのアンサンブルフレームワーク

Tathiana Mikamura Barchi1, João Lucas Ferreira Dos Santos1, Thiago Antonini Alves2

  • 1Graduate Program in Industrial Engineering, Federal University of Technology - Paraná, 84017-220, Ponta Grossa, Brazil.

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まとめ
この要約は機械生成です。

正確な風速予測は再生可能エネルギーにとって重要です。新しい遺伝的アルゴリズム（GA）ベースのアンサンブルフレームワークは風速予測を大幅に改善し、風力エネルギー統合の信頼性を高めます。

キーワード:
人工ニューラルネットワークBox & Jenkins法アンサンブル遺伝的アルゴリズムハイブリッドモデル風速予測

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科学分野:

  • 再生可能エネルギーシステム
  • 気象予測
  • 計算知能

背景:

  • 風力エネルギーは重要なクリーンリソースですが、その変動性には正確な予測が必要です。
  • 既存の風速予測モデルは気象の影響に対処するのに苦労しています。
  • 信頼性の高い予測は、風力エネルギーの断続性を管理するための鍵です。

研究 の 目的:

  • 強化された風速予測のための遺伝的アルゴリズム（GA）ベースのアンサンブルフレームワークを開発および評価すること。
  • 14の多様な予測モデルのパフォーマンスを体系的に比較すること。
  • 複数のブラジル都市におけるフレームワークの有効性を評価すること。

主な方法:

  • さまざまな予測モデルを組み合わせたGAベースのアンサンブルアプローチを提案しました。
  • 線形、ニューラルネットワーク、ハイブリッド、アンサンブルタイプを含む14のモデルを評価しました。
  • モデル検証のために、5つのブラジル都市からの毎分風速データを利用しました。

主要な成果:

  • GAベースのアンサンブルフレームワークは、低い平均二乗誤差（MSE）と平均絶対誤差（MAE）値で優れたパフォーマンスを示しました。
  • 高い決定係数（R^2）（0.7139–0.8723）は、堅牢な予測能力を示しました。
  • 統計的検証（Friedman検定、p < 0.001）により、モデルパフォーマンスの違いとランクの安定性が確認されました。

結論:

  • 提案されたGAベースのアンサンブルフレームワークは、風速予測精度において大幅な進歩を提供します。
  • このフレームワークは高いスケーラビリティと計算効率を示し、実用的なアプリケーションに適しています。
  • 風速予測の改善は、風力エネルギーシステムの統合と信頼性を向上させます。