10.1007/s40095-021-00408-x

A novel hybrid model based on weather variables relationships improving applied for wind speed forecasting

  • Hervice Roméo Fogno Fotso*1,
  • Claude Vidal Aloyem Kazé2,
  • Germaine Djuidje Kenmoé1,
  1. Labotatory of Mechanics, Department of Physics, University of Yaoundé I, Yaoundé, CM
  2. Department of Renewable Energy, HTTTC Kumba, University of Buea, Buea, CM

Published in Issue 2021-07-01

How to Cite

Fogno Fotso, H. R., Aloyem Kazé, C. V., & Djuidje Kenmoé, G. (2021). A novel hybrid model based on weather variables relationships improving applied for wind speed forecasting. International Journal of Energy and Environmental Engineering, 13(1 (March 2022). https://doi.org/10.1007/s40095-021-00408-x
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Abstract

Abstract Accurate wind speed forecasting is imperative for producing wind power integration. Thus, this paper presents a novel combined ARIMA—artificial neural network (ANN) forecasting model based on improved relationships between the wind speed and other weather variables pursuing to forecast wind speed. The weather variables on which the wind speed depends are transformed for normalization and relationships improving by using a proposed approach. The ARIMA models are employed for each transformed variables modeling and the unknown values forecasting. The measured wind speed is normalized and used as the target variable and the transformed weather variables as input variables to train the ANN model. The predicted weather variables are employed as input variables of trained ANN to forecast the unknown wind speed values. The proposed forecasting model has been validated with five different ANN structures for multi-step ahead wind speed forecasting from two Datasets in Bapouh, Cameroon. The experimental results indicate that the proposed data preprocessing strategy is appropriate to enhance the relationships between two variables and decrease the seasonal variation. Furthermore, the proposed hybrid model in terms of forecasting accuracy outperforms other comparable models.

Keywords

  • Wind speed forecasting,
  • Weather variables relationships,
  • Combined forecasting model,
  • Multi-step ahead forecasting,
  • Application

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