Earticle

초록

영어

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

목차

Abstract
1. INTRODUCTION
2. METHODOLOGY
2.1. Theoretical background of bivariate multistep timeseries forecasting
2.2. Data
2.3. Algorithms
2.4. Model building
2.5. Metrics
2.6. Research design
3. RESULT AND DISCUSSION
3.1. Error analysis
4. CONCLUSION
REFERENCES

키워드

저자

• Mulomba Mukendi Christian [ PhD candidate, Dept. of Advanced Convergence, Handong Global Univ., Korea ]
• Yun Seon Kim [ Associate Prof., School of Global Entrepreneurship and Information Communication Technology, Handong Global Univ., Korea ] Corresponding Author
• Hyebong Choi [ Asssociate Prof., School of Global Entrepreneurship and Information Communication Technology, Handong Global Univ., Korea ]
• Jaeyoung Lee [ Prof., School of Mechanical control and Engineering, Handong Global Univ., Korea ]
• SongHee You [ Assistant Prof., School of Spatial Environment System Engineering, Handong Global Univ., Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700