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

Author

Listed:
  • Chao-Ming Huang

    (Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan)

  • Shin-Ju Chen

    (Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan)

  • Sung-Pei Yang

    (Department of Green Energy Technology Research Center, Kun Shan University, Tainan 710, Taiwan)

  • Hsin-Jen Chen

    (Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan)

Abstract

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.

Suggested Citation

  • Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang & Hsin-Jen Chen, 2023. "One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods," Energies, MDPI, vol. 16(6), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2688-:d:1096134
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    References listed on IDEAS

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    1. Huang, Hao-Hsuan & Huang, Yun-Hsun, 2024. "Applying green learning to regional wind power prediction and fluctuation risk assessment," Energy, Elsevier, vol. 295(C).

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