Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme

Abstract

Wind energy analysis and wind speed modeling have a significant impact on wind power generation systems and have attracted significant attention from many researchers in recent decades. Based on the inherent characteristics of wind speed, such as nonlinearity and randomness, the prediction of wind speed is considered to be a challenging task. Previous studies have only considered point prediction or interval measurement of wind speed separately and have not combined these two methods for prediction and analysis. In this study, we developed a novel hybrid wind speed double prediction system comprising a point prediction module and interval prediction module to compensate for the shortcomings of existing research. Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. Based on point prediction results, we propose an interval prediction method that constructs different intervals according to the classification of different data features via fuzzy clustering, which provides reliable interval prediction results. e experimental results demonstrate that the proposed system outperforms existing methods in engineering applications and can be used as an effective technology for power system planning.

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

“…For this purpose, the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), sum square error (SSE) and 𝑅 2 metrics were used. The performance metrics are mathematically represented in (18) through (22) as follows:…”

“…Recently, AI-based and hybrid models have been developed to overcome the disadvantages of physical and statistical methods [12,18]. Since AI-based models do not require very precise wind information that might occur in an offshore environment, they outperform physical models in forecasting.…”

A Deep Convolutional Neural Network Model for Improving WRF Simulations

“…Unlike NWPs, statistical models require a large amount of historical data and completely neglect the physics of the atmosphere; thus, they do not consider meteorology [5], [12]. Since statistical methods are easily implemented and less computationally intensive than NWPs, they are popular among researchers.…”

Assessing the Significance of Relationship Between Metrology Variables under Indeterminacy

“…Damousis et al [17] studied the relationship between energy-related variables using fuzzy logic. Some statistical tests developed using the fuzzy logic can be seen in Montenegro et al [18], Petković [19], Grzegorzewski and Ś piewak [20], Sezer et al [21] and Nie et al [22].…”