One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods

Chao Ming Huang, Shin Ju Chen, Sung Pei Yang, Hsin Jen Chen

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)


This paper proposes an optimal ensemble method for one-day-ahead hourly wind power forecasting. The ensemble forecasting method is the most common method of meteorological forecasting. Several different forecasting models are combined to increase forecasting accuracy. The proposed optimal ensemble method has three stages. The first stage uses the k-means method to classify wind power generation data into five distinct categories. In the second stage, five single prediction models, including a K-nearest neighbors (KNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, a support vector regression (SVR) model, and a random forest regression (RFR) model, are used to determine five categories of wind power data to generate a preliminary forecast. The final stage uses an optimal ensemble forecasting method for one-day-ahead hourly forecasting. This stage uses swarm-based intelligence (SBI) algorithms, including the particle swarm optimization (PSO), the salp swarm algorithm (SSA) and the whale optimization algorithm (WOA) to optimize the weight distribution for each single model. The final predicted value is the weighted sum of the integral for each individual model. The proposed method is applied to a 3.6 MW wind power generation system that is located in Changhua, Taiwan. The results show that the proposed optimal ensemble model gives more accurate forecasts than the single prediction models. When comparing to the other ensemble methods such as the least absolute shrinkage and selection operator (LASSO) and ridge regression methods, the proposed SBI algorithm also allows more accurate prediction.

出版狀態Published - 2023 3月

All Science Journal Classification (ASJC) codes

  • 可再生能源、永續發展與環境
  • 燃料技術
  • 工程(雜項)
  • 能源工程與電力技術
  • 能源(雜項)
  • 控制和優化
  • 電氣與電子工程


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