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| Wind speed interval prediction using spatio-temporal fusion compressed residual networks with Bayesian optimized hyperparameters |
| DOI：10.19783/j.cnki.pspc.240596 |
| Key Words:wind speed prediction temporal convolutional network STiCDRS model GPR interval prediction Bayesian optimization |
| Author Name | Affiliation | | WU Yun1 | 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2. School of Environment, South China Normal University, Guangzhou 510630, China | | GE Jiamin1 | 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2. School of Environment, South China Normal University, Guangzhou 510630, China | | WANG Wenye1 | 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2. School of Environment, South China Normal University, Guangzhou 510630, China | | LI Xiaoyong2 | 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2. School of Environment, South China Normal University, Guangzhou 510630, China | | CHE Liang1 | 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2. School of Environment, South China Normal University, Guangzhou 510630, China |
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| Abstract:To address the challenge of high wind speed variability in wind farm planning, a small-sample-based spatio- temporal integration and compression deep residual point prediction model, spatio-temporal integration and compression deep residual (STiCDRS), is proposed. This model is designed to deeply explore the spatial and temporal characteristics within wind speed sequences to enhance the accuracy of point prediction. Initially, the spatio-temporal integration and compression deep residual network is employed to obtain point prediction results. Subsequently, an innovative hybrid model, STiCDRS-Gaussian process regression (STiCDRS-GPR), is introduced to achieve interval prediction results, thereby providing more reliable probabilistic forecasts of wind speed. The model utilizes a Bayesian optimization method for hyperparameter selection, ensuring efficient and automated tuning. Finally, the wind speed dataset from a wind farm in Inner Mongolia is used to compare the prediction results of the STiCDRS model with those of traditional classical models. Experimental results demonstrate that, in comparison to other models, the proposed STiCDRS-GPR model delivers superior point prediction accuracy, appropriate interval predictions, and reliable probabilistic forecasting outcomes, fully showcasing its considerable potential in the domain of wind speed forecasting. |
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