Multivariate selection-combination short-term wind speed forecasting system based on convolution-recurrent network and multi-objective chameleon swarm algorithm

Wind energy, as a typical environmentally friendly source of energy for power generation, has the advantages of being renewable and emitting no greenhouse gases. Moreover, in wind power generation, accurate wind speed prediction is vital. However, most existing forecasting models use only univariate time series forecasting models, ignoring the effect of other variables on wind speed and the improvement of the model using optimization al-gorithms, resulting in lower accuracy and stability. Aiming to fill this gap, we develop a complete multivariate selection-combination short-term wind speed forecasting system, which is composed of two advanced feature -selection methods; six single forecasting models based on convolutional and recurrent neural networks and a multi-objective chameleon swarm optimization algorithm. We prove theoretically that the proposed multi -objective chameleon swarm optimization algorithm has Pareto optimal solutions and performs best in some test functions by comparing it with other multi-objective swarm optimization algorithms. For wind speed pre-diction in summer, our proposed prediction system achieves a mean absolute percentage error of 1.937%, 2.110% and 2.584% in one-, two-and three-step forecasting, respectively, which is higher than single and other combined models.

Automated summary

As a renewable and environmentally friendly source of energy, wind energy is widely considered for power generation due to its emission-free and sustainable nature. Accurate wind speed prediction is crucial for effective wind power generation. However, existing forecasting models often overlook the influence of other variables on wind speed and lack optimization algorithms, which leads to lower accuracy and stability. To address this issue, we developed a comprehensive multivariate selection-combination short-term wind speed forecasting system, incorporating advanced feature selection methods, convolutional and recurrent neural networks, and a multi-objective chameleon swarm optimization algorithm. Our proposed system achieved significantly higher accuracy in one-step, two-step, and three-step wind speed prediction compared to single models and other combined models.