Accurate wind speed prediction is the key to large-scale application of wind energy in power systems, and the randomness and volatility of wind speed sequences make wind speed prediction more difficult. To enhance the predictability of wind speed sequences, a Logistic chaotic mapping strategy, adaptive parameter adjustment strategy, and the introduction of mutation strategy were used to improve the Carnivorous Plant Algorithm (CPA). A short-term wind speed prediction model based on error correction and VMD-ICPA-LSSVM was proposed. Firstly, meteorological factors are used as inputs for least squares support vector machine (LSSVM) to predict wind speed and obtain an error sequence. Then, K-L divergence is used to adaptively determine the parameters of Variational Mode Decomposition (VMD) and decompose the error sequence. Combining the Improved Carnivorous Plant Algorithm (ICPA) to optimize the adjustable parameters of LSSVM to predict the decomposed subsequences. After stacking the prediction results of each obtain the final wind speed prediction value. The experimental results show that subsequence, error correction is performed on the original prediction sequence to compared with other models, this model has better prediction accuracy and generalization performance.