Chapters authored
Comparison Study of AI-based Methods in Wind Energy
Wind energy forecasting is particularly important for wind farms due to cost-related issues, dispatch planning, and energy market operations. Thus, improving forecasting accuracy becomes an urgent task for researchers in the field of wind energy. However, there is limited research to discuss an overall comparison among various forecasting types, which is a foundation for future works with respect to wind energy prediction because this comparison may reveal whether there is a best model for a specific forecasting type or specific data in this field. For the purpose of laying a strong foundation for wind energy research, this chapter introduces five basic forecasting models, which are Autoregressive Moving Average Model (ARMA), Back-Propagation Neuron Network (BPNN), Support Vector Regression (SVR), Extreme Learning Machine (ELM), and Adaptive Network-Based Fuzzy Inference System (ANFIS) with implement codes before comparing the forecasting effectiveness of five different models in three wind farms based on five forecasting types. Comparison results indicate that each model has great divergent forecasting results in different wind farms and every forecasting type has its own “best model.”
Part of the book: New Applications of Artificial Intelligence