工程科学与技术2025，Vol.57Issue(3)：31-41,11.DOI:10.12454/j.jsuese.202300619
基于BWO优化VMD和TCN-BiGRU的短期风电功率预测
Short-term Wind Power Forecasting Based on BWO-VMD and TCN-BiGRU
摘要
Abstract
Objective Under the guidance of the"dual carbon"target,wind power,as a critical component of clean energy,plays a crucial role in its efficient utilization.Short-term wind power forecasting helps improve grid stability,optimize wind farm power generation plans,and reduce operating costs,enhancing the economic benefits of wind power and supporting the goals of low-carbon development.In addition,the prediction results provide valuable reference information for wind farms,assist the dispatching department in adjusting the generation plan in advance,reduce the impact of wind power grid connection on the grid,and ensure the safe operation of the power system.Given the instability and high volatility of wind power generation,this study proposes a short-term wind power prediction method based on BWO-VMD and TCN-BiGRU to improve the accuracy of wind power prediction and better support the energy transition under the"dual carbon"strategy. Methods A short-term wind power generation prediction model based on the beluga whale optimization(BWO)algorithm,variational mode de-composition(VMD),temporal convolutional network(TCN),and bidirectional gated recurrent unit(BiGRU)was carefully proposed to improve the prediction accuracy of wind power generation,particularly considering its inherent instability and high volatility.Firstly,considering the comprehensive and complex impact of various meteorological factors on wind power generation,the random forest(RF)method was employed.This involves a comprehensive process of carefully determining the importance of various meteorological factor characteristics,systematically and accurately ranking them,and then extracting the truly optimal features that have a significant impact on subsequent predictions.Secondly,VMD is effectively utilized to decompose raw power data,which is originally in a non-stationary sequence,into relatively stationary sub-sequences.However,due to its complexity,it is difficult to manually determine the two parameters.Therefore,the BWO algorithm began optimizing these parameters of VMD.On this basis,a comprehensive index combining sample entropy and VMD decomposition to reconstruct the errors of each order component is used as a fitness function.Through this method,a thorough search was conducted to identify the optima parameter combination.Then,the optimized VMD(OVMD)is utilized to decompose non-stationary power signals.Then,the decomposed sta-tionary subsequence is combined with carefully extracted optimal features and input into the TCN-BiGRU combination model for prediction.This combination model aims to use the advantages of TCN and BiGRU to process data and make more accurate predictions.Finally,the pre-dicted values of each subsequence are sequentially stacked to obtain the result,which is expected to provide reliable predictions for wind power generation. Results and Discussions The RF algorithm is strategically employed to screen meteorological features and systematically rank their importance,enabling the accurate selection of features that significantly impact wind power forecasting.The experimental results indicate that wind speeds at vertical heights of 10,30,and 50 m from the ground play an important role in influencing the accuracy of wind prediction.VMD is adopted to address the non-stationarity of wind power generation;however,manually determining its two parameters has proven to be challenging.Therefore,the BWO is proposed to optimize these parameters,with sample entropy and error reconstruction serving as key fitness function indicators.Compared to other optimization algorithms such as genetic algorithm(GA)and whale optimization algorithm(WOA),the BWO algorithm demonstrates significant per-formance,with faster running speed,stronger stability,and greater robustness.Then,the optimized VMD is utilized to decompose the non-stationary power signal,resulting in higher-quality subsequences and ultimately improving prediction accuracy.The dataset is carefully divided into a training set,a validation set,and a testing set to verify the accuracy of the model.This study selects a single model to compare the BiGRU network model and the OVMD-TCN-BiGRU combination model proposed in this study with other combination models for experimental a-nalysis.Through the graph,error evaluation indicators,and time indicators,the experimental results show that although the time of the proposed model is not optimal,its error evaluation indicator value is the smallest,highlighting its advantages.In addition,experiments are conducted not only on the main dataset but also extended to January and August data,which represent seasonal differences,for generalization to verify the re-liability and broad applicability of the model.The verification results indicate that the constructed model effectively handles various datasets and complex time series features,with strong robustness and generality,and can run stably and efficiently in various practical scenarios. Conclusions The decomposition of raw wind power data is primarily examined using the OVMD algorithm in this study.The meteorological factors selected by combining the decomposed sub-components with RF features are input into the TCN-BiGRU model for prediction.Their respective advantages are integrated to enhance the accuracy and stability of the prediction.The experimental results indicated that applying this series of methods improves the prediction accuracy.Its capacity to manage complex time series data is demonstrated,and the advancement and innovation of algorithms and models are supported.New directions for technological progress and practical application in the field of wind power prediction are established.关键词
短期风功率预测/变分模态分解/随机森林/时序卷积网络/双向门控循环单元/白鲸优化算法Key words
short-term wind power prediction/variational mode decomposition/random forest/temporal convolutional network/bidirectional gate recurrent unit/beluga whale optimization分类
信息技术与安全科学引用本文复制引用
逯静,张燕茹,王瑞..基于BWO优化VMD和TCN-BiGRU的短期风电功率预测[J].工程科学与技术,2025,57(3):31-41,11.基金项目
河南省科技攻关项目(222102210120) （222102210120）
国家自然科学基金项目(62273133) （62273133）
