TY  - EJOU
AU  - Ullah, Farhan 
AU  - Zhang, Xuexia 
AU  - Khan, Mansoor 
AU  - Abid, Muhammad 
AU  - Mohamed, Abdullah 

TI  - A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 79
IS  - 2
SN  - 1546-2226

AB  - Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporal neural network is enhanced by providing different input parameters including training layers, hidden and dropout layers along with activation and loss functions. The proposed framework is further analyzed by comparing state-of-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), respectively. The energy efficiency performance indicators showed that the proposed model demonstrates error reduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78% for RMSE for MERRA Wind farms 1, 2, and 3, respectively, compared to other existing methods. These quantitative results show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSE values ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in wind power forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for more sustainable energy solutions.
KW  - Ensemble learning; machine learning; real-time data analysis; stakeholder analysis; temporal convolutional network; wind power forecasting

DO  - 10.32604/cmc.2024.048656